on optimal gaussian signaling in mimo relay channels with partial decode and forward for gaussian multiple input multiple output mimo relay channels with partial decode and forward the optimal type of input distribution is still an open question in general recent research has revealed that in some other scenarios with unknown optimal input distributions e g interference channels improper i e noncircular gaussian distributions can outperform proper circular gaussian distributions in this paper we show that this is not the case for partial decode and forward in the gaussian mimo relay channel with gaussian transmit signals i e we show that a proper gaussian input distribution is the optimal one among all gaussian distributions in order to prove this property an innovation covariance matrix is introduced and a decomposition is performed by considering the optimization over this matrix as an outer problem a key point for showing optimality of proper signals then is a reformulation that reveals that one of the subproblems is equivalent to a sum rate maximization in a two user mimo broadcast channel under a sum covariance constraint for which the optimality of proper signals can be shown

design for self organizing fuzzy neural networks based on genetic algorithms a novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network named self organizing fuzzy neural network based on genetic algorithms sofnnga to implement takagi sugeno ts type fuzzy models is proposed in this paper a new adding method based on geometric growing criterion and the epsiv completeness of fuzzy rules is first used to generate the initial structure then a hybrid algorithm based on genetic algorithms backpropagation and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules this has two steps first the linear parameter matrix is adjusted and second the centers and widths of all membership functions are modified the ga is introduced to identify the least important neurons i e the least important fuzzy rules simulations are presented to illustrate the performance of the proposed algorithm

on stability of fuzzy systems expressed by fuzzy rules with singleton consequents addresses the issue of stability of a fuzzy system described by fuzzy rules with singleton consequents it first presents two canonical forms of a fuzzy system a parametric expression and a state space expression a fuzzy system with singleton consequents is found to be a piecewise polytopic affine system then the paper gives stability theorems for a discrete time system as well as for a continuous time system it also gives a brief survey on the stability issues of fuzzy control systems

radial basis function based adaptive fuzzy systems and their applications to system identification and prediction abstract in this paper we describe a neuro fuzzy system with adaptive capability to extract fuzzy if then rules from input and output sample data through learning the proposed system called radial basis function rbf based adaptive fuzzy system afs employs the gaussian functions to represent the membership functions of the premise part of fuzzy rules three architectural deviations of the rbf based afs are also presented according to different consequence types such as constant first order linear function and fuzzy variable these provide versatility of the network to handle arbitrary fuzzy inference schemes we present examples of system identification and time series prediction to illustrate how to solve these problems and to demonstrate its validity and effectiveness using the rbf based afs

fuzzy wavelet neural network models for prediction and identification of dynamical systems this paper presents fuzzy wavelet neural network fwnn models for prediction and identification of nonlinear dynamical systems the proposed fwnn models are obtained from the traditional takagi sugeno kang fuzzy system by replacing the then part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains the first and last model use summation and multiplication of dilated and translated versions of single dimensional wavelet basis functions respectively and in the second model then parts of the rules consist of radial function of wavelets gaussian type of activation functions are used in if part of the fuzzy rules a fast gradient based training algorithm i e the broyden fletcher goldfarb shanno method is used to find the optimal values for unknown parameters of the fwnn models simulation examples are also given to compare the effectiveness of the models with the other known methods in the literature according to simulation results we see that the proposed fwnn models have impressive generalization ability

joint optimization of the transmit covariance and relay precoder in general gaussian amplify and forward relay channels the maximum data rate that can be achieved by the strictly causal full duplex amplify and forward af scheme in general gaussian relay channels is achieved by gaussian codebooks and can be cast as the solution of an optimization problem of the input transmit covariance and relay precoder this problem possesses an intricate nonconvex structure and is hence difficult to solve to circumvent this difficulty the relay precoder is assumed to be given and then the karush kuhn tucker conditions are used to obtain closed form expressions for the optimal input covariance corresponding to that precoder these expressions are used to show that subdiagonal precoders suffice to attain the maximum achievable rate of the af scheme at any source transmit power in addition to significantly reducing the effort expended in searching for the optimal relay precoder this observation enables us to find the optimal precoders at low and high source transmit powers for asymptotically low transmit powers the optimal relaying mechanism is shown to possess an interlacing structure thereby resembling half duplex operation in contrast for asymptotically high transmit powers it is optimal for the relay to be silent the asymptotic analysis enables us to develop an explicit formulation for a suboptimal precoder that at intermediate source transmit powers are shown numerically to outperform asymptotically optimal precoders

the capacity of the semideterministic relay channel corresp the capacity of the class of relay channels with sender x 1 a relay sender x 2 a relay receiver y 1 f x 1 x 2 and ultimate receiver y is proved to be c max min p x 1 x 2 i x 1 x 2 y h y 1 x 2 i x 1 y x 2 y 1

design of a single input fuzzy logic controller and its properties abstract we suggest a simple but powerful flc fuzzy logic controller design method using a single fuzzy input variable which is equivalent to the pseudo sliding mode controller input variables of conventional flcs are mostly the error e and the change of error dot e regardless of the complexity of controlled plants a rule table is then constructed in a two dimensional input space the output of fuzzy inference is applied to the plant as the control input u or the change of control input u this scheme came from concepts of linear pd proportional derivative and pi proportional integral controllers we found that rule tables of most flcs have skew symmetry property and the absolute magnitude of the control input vbuvb or vb uvb is proportional to the distance from its main diagonal line in the normalized input space considering these facts we derive a new variable called the signed distance which is a sole fuzzy input variable in our simple flc called single input flc s flc the s flc has many advantages the total number of rules is greatly reduced compared to two dimensional flcs and hence generations and tuning of control rules are easy control performance is nearly the same as that of conventional flcs we also show that this s flc is equivalent to the pseudo smc sliding mode controller and hence the stability is guaranteed using the lyapunov stability the performance of s flc is revealed via computer simulations using a nonlinear plant

pseudo ϵ expansion and critical exponents of superfluid helium abstract the pseudo expansions series for critical exponents of the three dimensional x y model describing transition in liquid helium are derived up to the 6 terms numerical estimates extracted from the series obtained using the pade borel resummation technique scaling relations and the seven loop 7 estimate for the fisher exponent are presented including those for the exponents and measured in experiments with a record accuracy for the exponent the procedure argued to be most reliable gives 0 0117 the number that is very close to the most accurate experimental values it signals that the pseudo expansion approach is a powerful tool robust enough to evaluate critical exponents with small absolute errors the arguments in favor of such a robustness are presented

bilayer low density parity check codes for decode and forward in relay channels this paper describes an efficient implementation of binning for decode and forward df in relay channels using low density parity check ldpc codes bilayer ldpc codes are devised to approach the theoretically promised rate of the df relaying strategy by incorporating relay generated parity bits in specially designed bilayer graphical code structures while conventional ldpc codes are sensitively tuned to operate efficiently at a certain channel parameter the proposed bilayer ldpc codes are capable of working at two different channel parameters and two different rates that at the relay and at the destination to analyze the performance of bilayer ldpc codes bilayer density evolution is devised as an extension of the standard density evolution algorithm based on bilayer density evolution a design methodology is developed for the bilayer codes in which the degree distribution is iteratively improved using linear programming further in order to approach to the theoretical df rate for a wide range of channel parameters this paper proposes two different forms of bilayer codes the bilayer expurgated and bilayer lengthened codes it is demonstrated that the rate of a properly designed bilayer ldpc code can closely approach the theoretical df limit finally it is shown that a generalized version of the proposed bilayer code construction is applicable to relay networks with multiple relays

an output constrained clustering approach for the identification of fuzzy systems and fuzzy granular systems this paper presents an output constrained clustering approach for fuzzy system identification and fuzzy granular system identification the approach is unlike most existing clustering algorithms for structure identification of fuzzy systems where the focus is on input or combined input output clustering the output constrained clustering algorithm divides the output space into several partitions and each output partition is considered to be a constraint then input data are projected into clusters that are based on the input distribution constrained by the output partitions by introducing the key concept of separability of a set of clusters within each output constraint the proposed approach automatically finds an appropriate small and efficient number of clusters for each output constraint to have an appropriate small and efficient number of clusters in each output constraint results in a more compact final system structure and better accuracy this better performance is illustrated by experiments using benchmark function approximation and dynamic system identification

narmax time series model prediction feedforward and recurrent fuzzy neural network approaches the nonlinear autoregressive moving average with exogenous inputs narmax model provides a powerful representation for time series analysis modeling and prediction due to its capability of accommodating the dynamic complex and nonlinear nature of real world time series prediction problems this paper focuses on the modeling and prediction of narmax model based time series using the fuzzy neural network fnn methodology both feedforward and recurrent fnns approaches are proposed furthermore an efficient algorithm for model structure determination and parameter identification with the aim of producing improved predictive performance for narmax time series models is developed experiments and comparative studies demonstrate that the proposed fnn approaches can effectively learn complex temporal sequences in an adaptive way and they outperform some well known existing methods

route to chaos and some properties in the boundary crisis of a generalized logistic mapping abstract a generalization of the logistic map is considered showing two control parameters and that can reproduce different logistic mappings including the traditional second degree logistic map cubic quartic and all other degrees we introduce a parametric perturbation such that the original logistic map control parameter r changes its value periodically according an additional parameter 2 q the value of q gives this period for this system an analytical expression is obtained for the first bifurcation that starts a period doubling cascade and using the feigenbaum universality we found numerically the accumulation point r c where the cascade finishes giving place to chaos in the second part of the paper we study the death of this chaotic behavior due to a boundary crisis at the boundary crisis orbits can reach a maximum value x x max 1 when it occurs the trajectory is mapped to a fixed point at x 0 we show that there exist a general recursive formula for initial conditions that lead to x x max

gaussian orthogonal relay channels optimal resource allocation and capacity a gaussian orthogonal relay model is investigated where the source transmits to the relay and destination in channel 1 and the relay transmits to the destination in channel 2 with channels 1 and 2 being orthogonalized in the time frequency plane in order to satisfy practical constraints the total available channel resource time and bandwidth is split into the two orthogonal channels and the resource allocation to the two channels is considered to be a design parameter that needs to be optimized the main focus of the analysis is on the case where the source to relay link is better than the source to destination link which is the usual scenario encountered in practice a lower bound on the capacity achievable rate is derived and optimized over the parameter spl theta which represents the fraction of the resource assigned to channel 1 it is shown that the lower bound achieves the max flow min cut upper bound at the optimizing spl theta the common value thus being the capacity of the channel at the optimizing spl theta furthermore it is shown that when the relay to destination signal to noise ratio snr is less than a certain threshold the capacity at the optimizing spl theta is also the maximum capacity of the channel over all possible resource allocation parameters spl theta finally the achievable rates for optimal and equal resource allocations are compared and it is shown that optimizing the resource allocation yields significant performance gains

on the relay channel with receiver transmitter feedback an achievable rate for the discrete memoryless relay channel with receiver transmitter feedback is proposed based on block markov superposition encoding the achievable rate can also be extended to gaussian channels a second achievable rate for the gaussian relay channel based on a schalkwijk kailath type scheme is presented for some channels both achievable rates strictly improve upon all previously known achievable rates for the discrete memoryless relay channel also a converse result is provided

robust stability analysis and systematic design of single input interval type 2 fuzzy logic controllers recent results on fuzzy control have shown that interval type 2 it2 fuzzy logic controllers flcs might achieve better control performance due to the additional degree of freedom provided by the footprint of uncertainty fou in their it2 fuzzy sets however the design and robust stability analysis of the it2 flcs are still challenging problems due to their relatively more complex internal structure in this paper we will derive the explicitly fuzzy mapping fm of a single input it2 flc sit2 flc to present design methods and investigate its robustness the analytical information of the it2 fm will give the opportunity to provide explanations on the roles of the fou parameters by taking advantage of the well developed framework of nonlinear control theory comparative theoretical explorations will be presented on the differences between the type 1 t1 fm and it2 fm to clearly show the role of the fou on the robust control system performance it will be proven that the robust stability of the it2 fuzzy system is guaranteed with the aids of the well known popov lyapunov method moreover analytical design methods are presented for sit2 flcs to generate commonly employed control curves by only tuning the size of the fous without a need of an optimization procedure it will be theoretically shown that the fou gives the opportunity to the sit2 flc to generate commonly employed nonlinear control curves while also providing a certain degree of robustness which cannot be accomplished by its t1 counterpart the presented results provide theoretical explanations on the role of the fou on the performance and robustness of the sit2 flc

effective characteristic polynomials and two point pade approximants as summation techniques for the strongly divergent perturbation expansions of the ground state energies of anharmonic oscillators pad e approximants are able to sum effectively the rayleigh schr odinger perturbation series for the ground state energy of the quartic anharmonic oscillator as well as the corresponding renormalized perturbation expansion e j weniger j ifmmode check c else v c fi i ifmmode check z else v z fi ek and f vinette j math phys 34 571 1993 in the sextic case pad e approximants are still able to sum these perturbation series but convergence is so slow that they are computationally useless in the octic case pad e approximants are not powerful enough and fail on the other hand the inclusion of only a few additional data from the strong coupling domain e j weniger ann phys ny to be published greatly enhances the power of summation methods the summation techniques which we consider are two point pad e approximants and effective characteristic polynomials it is shown that these summation methods give good results for the quartic and sextic anharmonic oscillators and even in the case of the octic anharmonic oscillator which represents an extremely challenging summation problem two point pad e approximants give relatively good results textcopyright 1996 the american physical society

practical quantizer design for half duplex estimate and forward relaying we propose a quantizer design method for practical half duplex estimate and forward ef relaying first we identify the regime in which ef relaying yields substantial gains where the snr is low and the relay destination link is strong then we discover design simplifications that reduce complexity with little loss in the above regime for relay quantizer design we first consider mean squared distortion minimization to illustrate the unsuitability of the approach we present an example with awgn links and a bpsk source where the quantizer with worst mean squared distortion in a given set maximizes achievable rate a distortion minimizing quantizer attempts to preserve the received signal at the relay the quantizer should instead preserve source information in information theoretical terms the quantizer should maximize the mutual information between the source transmission and the quantizer output conditioned on the side information at the destination subject to a rate constraint the above conclusion derived from information theoretical principles is then translated to a quantizer design method for the low snr regime using ldpc codes of blocklength 100000 bpsk modulation and quantizers designed using the proposed criterion we observe performance less than a decibel away from the achievable rate at a ber of 10 4

realization of nonlinear and linear pid controls using simplified indirect fuzzy inference method abstract in this paper we propose a more practical method for realizing nonlinear and linear pid controls using fuzzy inference methods than previous ones this proposed method is based on the fact that not only nonlinear but also linear pid control can be easily realized by applying the simplified indirect fuzzy inference method for fuzzy control to few fuzzy control rules with a simple structure therefore we first show that nonlinear pid control which is based on a conventional pid controller can be realized using this proposed method secondly we show that linear pid control can also be realized simply by replacing the arctan with the linear membership function in the antecedent part of the fuzzy control rules

truncated chebyshev series approximation of fuzzy systems for control and nonlinear system identification the lack of mathematical models that pertains fuzzy control systems imposes a serious drawback regarding some important tasks such as stability analysis and system identification for that purpose the availability of analytical expressions for the fuzzy systems is very important this paper provides a systematic practical way of approximating fuzzy systems by chebyshev polynomials which depend on a finite number of parameters the proposed methodology is illustrated with two examples a a control problem concerning a tubular reactor which depending on the operating conditions may exhibit multiple steady states and b the problem of identifying a continuous stirred tank reactor which at certain values of structural parameters exhibits stable or unstable steady states or limit cycles

on the reliability function of the discrete memoryless relay channel bounds on the reliability function for the discrete memoryless relay channel are derived using the method of types two achievable error exponents are derived based on partial decode forward and compress forward which are well known superposition block markov coding schemes the derivations require combinations of the techniques involved in the proofs of csiszar korner marton s packing lemma for the error exponent of channel coding and marton s type covering lemma for the error exponent of source coding with a fidelity criterion the decode forward error exponent is evaluated on sato s relay channel from this example it is noted that to obtain the fastest possible decay in the error probability for a fixed effective coding rate one ought to optimize the number of blocks in the block markov coding scheme assuming the blocklength within each block is large an upper bound on the reliability function is also derived using ideas from haroutunian s lower bound on the error probability for point to point channel coding with feedback

an art based construction of rbf networks radial basis function rbf networks are widely used for modeling a function from given input output patterns however two difficulties are involved with traditional rbf trbf networks the initial configuration of an rbf network needs to be determined by a trial and error method and the performance suffers when the desired output has abrupt changes or constant values in certain intervals we propose a novel approach to over come these difficulties new kernel functions are used for hidden nodes and the number of nodes is determined automatically by an adaptive resonance theory art like algorithm parameters and weights are initialized appropriately and then tuned and adjusted by the gradient descent method to improve the performance of the network experimental results have shown that the rbf networks constructed by our method have a smaller number of nodes a faster learning speed and a smaller approximation error than the networks produced by other methods

designing fuzzy inference systems from data an interpretability oriented review fuzzy inference systems fis are widely used for process simulation or control they can be designed either from expert knowledge or from data for complex systems fis based on expert knowledge only may suffer from a loss of accuracy this is the main incentive for using fuzzy rules inferred from data designing a fis from data can be decomposed into two main phases automatic rule generation and system optimization rule generation leads to a basic system with a given space partitioning and the corresponding set of rules system optimization can be done at various levels variable selection can be an overall selection or it can be managed rule by rule rule base optimization aims to select the most useful rules and to optimize rule conclusions space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters structure optimization is of a major importance selecting variables reducing the rule base and optimizing the number of fuzzy sets over the years many methods have become available for designing fis from data their efficiency is usually characterized by a numerical performance index however for human computer cooperation another criterion is needed the rule interpretability an implicit assumption states that fuzzy rules are by nature easy to be interpreted this could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts the paper analyzes the main methods for automatic rule generation and structure optimization they are grouped into several families and compared according to the rule interpretability criterion for this purpose three conditions for a set of rules to be interpretable are defined

diversity analysis of randomized linear dispersion codes in a half duplex amplify and forward multiple relay system a point to point multiple relay communication system with half duplex constraints is considered the relays operate under the amplify and forward paradigm and implement a linear dispersion distributed space time code with randomized dispersion matrices of independent and identically distributed entries the large signal to noise ratio behavior of the asymptotically large deterministic system is studied for both the optimum maximum likelihood receiver and the linear minimum mean squared error lmmse receiver the diversity order of the system is shown to have a strong dependence on the aspect ratio of the linear dispersion matrices more specifically when the linear dispersion matrices are sufficiently tall both receivers achieve the maximum diversity order i e the total number of relays plus one conversely when the linear dispersion matrices are fat in the sense that relays linearly compress the information received from the source the optimum receiver is shown to achieve an asymptotic diversity order of two whereas the lmmse receiver is totally unable to exploit the available spatial diversity

decorrelation of neutral vector variables theory and applications in this paper we propose novel strategies for neutral vector variable decorrelation two fundamental invertible transformations namely serial nonlinear transformation and parallel nonlinear transformation are proposed to carry out the decorrelation for a neutral vector variable which is not multivariate gaussian distributed the conventional principal component analysis cannot yield mutually independent scalar variables with the two proposed transformations a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom we also evaluate the decorrelation performances for the vectors generated from a single dirichlet distribution and a mixture of dirichlet distributions the mutual independence is verified with the distance correlation measurement the advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

learning control using fuzzified self organizing radial basis function network this note describes an approach to integrating fuzzy reasoning systems with radial basis function rbf networks and shows how the integrated network can be employed as a multivariable self organizing and self learning fuzzy controller in particular by drawing some equivalence between a simplified fuzzy control algorithm sfca and a rbf network we conclude that the rbf network can be interpreted in the context of fuzzy systems and can be naturally fuzzified into a class of more general networks referred to as fbfn with a variety of basis functions not necessarily globally radial synthesized from each dimension by fuzzy logical operators on the other hand as a result of natural generalization from rbf to sfca we claim that the fuzzy system like rbf is capable of universal approximation next the fbfn is used as a multivariable rule based controller but with an assumption that no rule base exists leading to a challenging problem of how to construct such a rule base directly from the control environment we propose a simple and systematic approach to performing this task by using a fuzzified competitive self organizing scheme and incorporating an iterative learning control algorithm into the system we have applied the approach to a problem of multivariable blood pressure control with a fbfn based controller having six inputs and two outputs representing a complicated control structure

bounds on capacity and minimum energy per bit for awgn relay channels upper and lower bounds on the capacity and minimum energy per bit for general additive white gaussian noise awgn and frequency division awgn fd awgn relay channel models are established first the max flow min cut bound and the generalized block markov coding scheme are used to derive upper and lower bounds on capacity these bounds are never tight for the general awgn model and are tight only under certain conditions for the fd awgn model two coding schemes that do not require the relay to decode any part of the message are then investigated first it is shown that the side information coding scheme can outperform the block markov coding scheme it is also shown that the achievable rate of the side information coding scheme can be improved via time sharing in the second scheme the relaying functions are restricted to be linear the problem is reduced to a single letter nonconvex optimization problem for the fd awgn model the paper also establishes a relationship between the minimum energy per bit and capacity of the awgn relay channel this relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energy per bit that do not differ by more than a factor of 1 45 for the fd awgn relay channel model and 1 7 for the general awgn model

a global local optimization approach to parameter estimation of rbf type models we present a hybrid global local optimization algorithm for parameter estimation of radial basis function rbf networks and the rbf type autoregressive models without exogenous inputs rbf ar or with exogenous inputs rbf arx the rbf ar x models are quasi linear time varying ar x models with gaussian rbf network style coefficients which have been used to effectively model the nonlinear behavior of various complex systems however the identification of these models is a difficult optimization problem because of the large number of local minima a hybrid approach is proposed in this paper to achieve better optimization results for these rbf type models the applied hybrid search strategy ea snpom is developed by combining an evolutionary algorithm ea with a gradient based algorithm known as the structured nonlinear parameter optimization method snpom this strategy makes use of the robustness of the ea to cover an entire global search space and the efficiency of the gradient search to converge to a local optimum several examples of time series modeling and system identification are presented the simulation results indicate that the performance of the proposed hybrid approach is better than the performance obtained from using each method ea or snpom alone furthermore the rbf ar x models estimated by the ea snpom achieve much better modeling accuracy relative to other neural networks or fuzzy models in the simulations

a new approach of neuro fuzzy learning algorithm for tuning fuzzy rules abstract in this paper we develop a new approach of neuro fuzzy learning algorithm for tuning fuzzy rules by using training input output data based on the gradient descent method a major advantage of this approach is that fuzzy rules or membership functions can be learned without changing the form of fuzzy rule table used in usual fuzzy applications so that the case of non firing or weak firing can be well avoided which is different from the conventional neuro fuzzy learning algorithms moreover some properties of the developed approach are also discussed finally the efficiency of the developed approach is illustrated by means of identifying non linear functions

dynamical membership functions an approach for adaptive fuzzy modelling new approaches in fuzzy modelling in order to solve practical limitations found in classic adaptive fuzzy modelling are considered an interesting contribution in the fuzzy logic field in this work an approach for the development of dynamical fuzzy models is presented the approach allows to incorporate the system dynamics into the fuzzy membership functions which are defined in terms of the sample mean value and the variance of each variable of the fuzzy model from the input and output values of the system to be modelled these fuzzy membership functions defined as dynamical functions have adjustable parameters which are adapted by using a conventional off line gradient descent based algorithm in this way after the learning process the resulted fuzzy model has non static membership functions dependent on the available values of the variables at time t some application examples to illustrate the performance of the proposed dynamical adaptive fuzzy model on system identification are presented and the experimental results are discussed in order to remark the capabilities of the new fuzzy model

on the equivalence conditions of fuzzy inference methods part 1 basic concept and definition this paper addresses equivalence of fuzzy inference methods it first presents several well known fuzzy inference methods the product sum gravity method the simplified fuzzy inference method the fuzzy singleton type inference method the single input rule modules connected type fuzzy inference method sirms method and the single input connected fuzzy inference method sic method second three fuzzy inference methods of the product sum gravity method simplified fuzzy inference method and fuzzy singleton type inference method which are all widely used as fuzzy control methods are shown to be equivalent to each other third the equivalence conditions between the sirms method and the sic method known as single input type fuzzy inference method are shown finally it also gives the equivalence conditions between the single input type fuzzy inference methods and the previous three fuzzy inference methods investigating the equivalence among various fuzzy inference methods would help to understand the relationship of those fuzzy inference methods

1 n expansion for the number of matchings on regular graphs and monomer dimer entropy using a 1 n expansion that is an expansion in descending powers of n for the number of matchings in regular graphs with 2n vertices we study the monomer dimer entropy for two classes of graphs we study the difference between the extensive monomer dimer entropy of a random r regular graph g bipartite or not with 2n vertices and the average extensive entropy of r regular graphs with 2n vertices in the limit n rightarrow infty we find a series expansion for it in the numbers of cycles with probability 1 it converges for dimer density p 1 and for g bipartite it diverges as mathrm ln 1 p for p rightarrow 1 in the case of regular lattices we similarly expand the difference between the specific monomer dimer entropy on a lattice and the one on the bethe lattice we write down its taylor expansion in powers of p through the order 10 expressed in terms of the number of totally reducible walks which are not tree like we prove through order 6 that its expansion coefficients in powers of p are non negative

a self evolving interval type 2 fuzzy neural network with online structure and parameter learning this paper proposes a self evolving interval type 2 fuzzy neural network seit2fnn with online structure and parameter learning the antecedent parts in each fuzzy rule of the seit2fnn are interval type 2 fuzzy sets and the fuzzy rules are of the takagi sugeno kang tsk type the initial rule base in the seit2fnn is empty and the online clustering method is proposed to generate fuzzy rules that flexibly partition the input space to avoid generating highly overlapping fuzzy sets in each input variable an efficient fuzzy set reduction method is also proposed this method independently determines whether a corresponding fuzzy set should be generated in each input variable when a new fuzzy rule is generated for parameter learning the consequent part parameters are tuned by the rule ordered kalman filter algorithm for high accuracy learning performance detailed learning equations on applying the rule ordered kalman filter algorithm to the seit2fnn consequent part learning with rules being generated online are derived the antecedent part parameters are learned by gradient descent algorithms the seit2fnn is applied to simulations on nonlinear plant modeling adaptive noise cancellation and chaotic signal prediction comparisons with other type 1 and type 2 fuzzy systems in these examples verify the performance of the seit2fnn

structure identification of fuzzy model abstract the problems of structure identification of a fuzzy model are formulated a criterion for the verification of a structure is discussed using the criterion an algorithm for identifying a structure is suggested further a successive identification algorithm of the parameters is suggested the proposed methods are applied to an example

pade approximant for hard sphere square well and hard sphere square well square shoulder model fluids abstract the coupling parameter series expansion cpse is used to calculate coefficients a i i 1 2 3 7 of the high temperature series expansion htse for two hard sphere square well models and two hard sphere square well square shoulder models the four sets of coefficients are used to construct 28 pade approximant variants p n d for the four models based on the 28 pade variants six common thermodynamic functions for the four models are calculated and compared with corresponding results based on mc simulations reported in the literature and the htse truncated to seventh order in the present work respectively several conclusions are generalized from the comparisons i the commonly used rule of the minimum value for n d to choose the best pade approximation may not select the best one which depends on the model and relevant parameters values and has to be determined in advance by reference to simulations ii from the whole table of 28 pade approximants reliable throughout the text by reliable we mean the results are at least qualitatively correct variants always can be singled out which not only provides an improvement over the raw htse truncated at the optimal order but also is free of pole s and consequently applicable over reasonably wide phase space of interest particularly the improvement can be immense for most but not all of the models considered when the excess heat capacity is being considered iii among the reliable p n d approximations picked some have high values of n d indicating that the higher order htse coefficients calculated by the cpse provide important and reliable information for the unknown excess helmholtz free energy and the conventional pade summation indeed provides an effective method to utilize the information iv the pade summation can be a resort in statistical mechanics perturbation theories of simple potential fluids because of its simplicity in the actual application and possibility in improving the raw htse however to ensure the use reliably one comprehensive pre research should be carried out

an online gradient based parameter identification algorithm for the neuro fuzzy systems abstract online gradient descent method has been widely applied for parameter learning in neuro fuzzy systems the success of the application relies on the convergence of the learning procedure however there barely have been convergence analyses on the online learning procedure for neuro fuzzy systems in this paper an online gradient learning algorithm with adaptive learning rate is proposed to identify the parameters of the neuro fuzzy systems representing the mamdani fuzzy model with gaussian fuzzy sets we take the reciprocals of the variances of the gaussian membership functions rather than the variances themselves as independent variables when computing the gradient with respect to the variance parameters subsequently oscillation of the gradient value in the learning process can be avoided furthermore some convergence results for this online learning scheme are studied finally three numerical examples are provided to illustrate the performance of the proposed algorithm

analysis and design of functionally weighted single input rule modules connected fuzzy inference systems the single input rule modules sirms connected fuzzy inference method can efficiently solve the fuzzy rule explosion phenomenon which usually occurs in the multivariable modeling and or control applications however the performance of the sirms connected fuzzy inference system sirm fis is limited due to its simple input output mapping in this paper to further enhance the performance of sirm fis a functionally weighted sirm fis fwsirm fis which adopts multivariable functional weights to measure the important degrees of the sirms is presented then in order to show the fundamental differences of the sirms methods properties of the traditional sirm fis the type 2 sirm fis t2sirm fis the functional sirm fis fsirm fis the sirms model with single variable functional weights sirm fw and fwsirm fis are explored these properties demonstrate that the proposed fwsirm fis has more general and complex input output mapping than the existing sirms methods such properties theoretically guarantee that better performance can be achieved by fwsirm fis furthermore based on the least squares method a novel data driven optimization method is presented for the parameter learning of fwsirm fis it can also be used to optimize the parameters of sirm fis t2sirm fis fsirm fis and sirm fw due to the properties of the least squares method the proposed parameter learning algorithm can overcome the drawbacks of the gradients based parameter learning methods and obtain both smallest training errors and smallest parameters finally to show the effectiveness and superiority of fwsirm fis and the proposed optimization method six examples and detailed comparisons are given simulation results show that fwsirm fis can obtain better performance than the other sirms methods and compared with some well known methods fwsirm fis can achieve similar or better performance but has much less parameters and faster training speed

identification and simplification of t s fuzzy neural networks based on incremental structure learning and similarity analysis abstract this paper proposes a novel identification method and a simplification scheme for t s fuzzy neural networks which consists of two steps the first step refers to the structure design based on an incremental clustering approach whose basic ideas are that the structure identification of fuzzy neural networks is guided by the attenuation of output approximation error in each cluster and processed by a recursive refined clustering iteration with the input space clustering and sub clustering as the main steps once the structure of a t s fuzzy neural network is identified by the incremental clustering approach its parameters are further learned and refined by the levenberg marquardt optimization algorithm the second step refers to the structure simplification including removing redundant fuzzy rules and merging highly similar fuzzy rules furthermore the performances of several similarity calculating methods are analyzed and discussed which provides a basis for the selection of the appropriate similarity analysis and effective calculating method for the merging of fuzzy rules and system simplification that is the given performance analysis provides the methodology basis and design guide for structure simplification based on similarity analysis and merger of fuzzy rules several experiments are implemented to illustrate the feasibility and effectiveness of the proposed approach

a joint time invariant filtering approach to the linear gaussian relay problem in this paper the linear gaussian relay problem is considered under the linear time invariant lti model the rate maximization problem in the linear gaussian relay channel is formulated in the frequency domain based on the toeplitz distribution theorem under the further assumption of realizable input spectra the rate maximization problem is converted to the problem of joint source and relay filter design with two power constraints one at the source and the other at the relay and a practical solution to this problem is proposed based on the adaptive projected sub gradient method numerical results show that the proposed method yields a considerable gain over the instantaneous amplify and forward af scheme in inter symbol interference isi channels also the optimality of the af scheme within the class of one tap relay filters is established in flat fading channels

iterative decoding threshold analysis for ldpc convolutional codes an iterative decoding threshold analysis for terminated regular ldpc convolutional ldpcc codes is presented using density evolution techniques the convergence behavior of an iterative belief propagation decoder is analyzed for the binary erasure channel and the awgn channel with binary inputs it is shown that for a terminated ldpcc code ensemble the thresholds are better than for corresponding regular and irregular ldpc block codes

bilayer ldpc convolutional codes for decode and forward relaying in this paper we present bilayer ldpc convolutional codes for half duplex relay channels two types of codes bilayer expurgated ldpc convolutional codes and bilayer lengthened ldpc convolutional codes are proposed for decode and forward df relaying in the case of the binary erasure relay channel we prove analytically that both code constructions achieve the capacities of the source relay link and the source destination link simultaneously provided that the channel conditions are known when designing the codes meanwhile both codes enable the highest transmission rate possible with df relaying for a wide range of channel parameters in addition the regular degree distributions can easily be computed from the channel parameters which significantly simplifies the code optimization the code construction and performance analysis are extended to the general binary memoryless symmetric channel where a capacity achieving performance is conjectured numerical results are provided for both types of codes with finite node degrees over binary erasure channels and binary input additive white gaussian noise channels which verify the aforementioned theoretical analysis

fuzzy sets as a basis for a theory of possibility the theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable more specifically if f is a fuzzy subset of a universe of discourse u u which is characterized by its membership function f then a proposition of the form x is f where x is a variable taking values in u induces a possibility distribution tx which equates the possibility of x taking the value u to f u the compatibility of u with f in this way x becomes a fuzzy variable which is associated with the possibility distribution tx in much the same way as a random variable is associated with a probability distribution in general a variable may be associated both with a possibility distribution and a probability distribution with the weak connection between the two expressed as the possibility probability consistency principle a thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is in the main possibilistic rather than probabilistic in nature thus by employing the concept of a possibility distribution a proposition p in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p several types of conditional translation rules are discussed and in particular a translation rule for propositions of the form x is f is possible where is a number in the interval 0 1 is formulated and illustrated by examples

knowledge discovery by a neuro fuzzy modeling framework in this paper a neuro fuzzy modeling framework is proposed which is devoted to discover knowledge from data and represent it in the form of fuzzy rules the core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base through a two phase learning of a neuro fuzzy network in order to obtain reliable and readable knowledge two further stages are integrated with the knowledge extraction procedure a pre processing stage performing variable selection on the available data to obtain simpler and more reliable fuzzy rules and a post processing stage that granulates outputs of the extracted fuzzy rules so as to provide a validity range of estimated outputs moreover the framework can address complex multi input multi output problems in such case two distinct modeling strategies can be followed with the opportunity of producing both a single mimo model or a collection of miso models the proposed framework is verified on a real world case study involving prediction of chemical composition of ashes produced by combustion processes carried out in thermo electric generators located in italy

a new neuro fds definition for indirect adaptive control of unknown nonlinear systems using a method of parameter hopping the indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper the method is based on a new neuro fuzzy dynamical system neuro fds definition which uses the concept of adaptive fuzzy systems afss operating in conjunction with high order neural network functions fhonnfs since the plant is considered unknown we first propose its approximation by a special form of an fds and then the fuzzy rules are approximated by appropriate honnfs thus the identification scheme leads up to a recurrent high order neural network rhonn which however takes into account the fuzzy output partitions of the initial fds the proposed scheme does not require a priori experts information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions once the system is identified around an operation point it is regulated to zero adaptively weight updating laws for the involved honnfs are provided which guarantee that both the identification error and the system states reach zero exponentially fast while keeping all signals in the closed loop bounded the existence of the control signal is always assured by introducing a novel method of parameter hopping which is incorporated in the weight updating law simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given also the applicability of the method is tested on a direct current dc motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation

state dependent relay channel achievable rate and capacity of a semideterministic class this paper considers the problem of communicating over a relay channel with state when noncausal state information is partially available at the nodes we first establish a lower bound on the achievable rates based on noisy network coding and gelfand pinsker coding and show that it provides an alternative characterization of a previously known bound we then introduce the class of state decoupled relay channels and show that our lower bound is tight for a subclass of semideterministic channels we also compute the capacity for two specific examples of this subclass a channel with multiplicative binary fading and a channel with additive gaussian interference these examples are not special cases of previous classes of semideterministic relay channels with known capacity

an architecture of neural networks with interval weights and its application to fuzzy regression analysis abstract in this paper we first propose an architecture of neural networks that have interval weights and interval biases a neural network with the proposed architecture maps an input vector of real numbers to an output interval the target output is also given as an interval next we define a cost function using the interval output from the neural network and the corresponding target output a learning algorithm is derived from the cost function in a similar manner as the bp back propagation algorithm we also show two variations of the learning algorithm for the proposed architecture which lead to the inclusion relation between the interval output and the interval target last we apply the learning algorithms to fuzzy regression analysis where target outputs are given as fuzzy numbers

study of gaussian relay channels with correlated noises in this paper we consider the full duplex and half duplex gaussian relay channels where the noises at the relay and destination are arbitrarily correlated we first derive the capacity upper bound and the achievable rates with three existing schemes decode and forward df compress and forward cf and amplify and forward af we present two capacity results under specific noise correlation coefficients one being achieved by df and the other being achieved by direct link transmission or a special case of cf the channel for the former capacity result is equivalent to the traditional gaussian degraded relay channel and the latter corresponds to the gaussian reversely degraded relay channel for cf and af schemes we show that their achievable rates are strictly decreasing functions of the correlation coefficient when the correlation coefficient is negative moreover when the noise correlation coefficient is positive the cf achievable rate may also outperform the independent noise case if the noise correlation coefficient is within a certain range through numerical comparisons under different channel settings we observe that although df completely disregards the noise correlation while the other two can potentially exploit such extra information none of the three relay schemes always outperforms the others over different correlation coefficients moreover the exploitation of noise correlation by cf and af accrues more benefit when the source relay link is weak this paper also considers the optimal power allocation problem under the correlated noise channel setting with individual power constraints at the relay and the source it is shown that the relay should use all its available power to maximize the achievable rates under any correlation coefficient with a total power constraint across the source and the relay the achievable rates are proved to be concave functions over the power allocation factor for af and cf under full duplex mode where the closed form power allocation strategy is derived

some considerations on conventional neuro fuzzy learning algorithms by gradient descent method in this paper we try to analyze several conventional neuro fuzzy learning algorithms which are widely used in recent fuzzy applications for tuning fuzzy rules and give a summarization of their properties in detail some of these properties show that the uses of the conventional neuro fuzzy learning algorithms are difficult or inconvenient sometimes for constructing an optimal fuzzy system model in practical fuzzy applications

analysis of the generalized df cf for gaussian relay channels decode or compress we consider a three node quasi static communication system with a full duplex relay the goal is to determine the relaying mode that enables rate efficient communication under given channel conditions to achieve this goal we consider a generalized scheme that subsumes the decode and forward df and compress and forward cf schemes as special cases the generalized scheme is considered when the source and relay signals are synthesized from commonly used gaussian codebooks which are shown to be capacity achieving in two asymptotic cases perfect relay destination link and broken source destination link studying the generalized df cf scheme it is shown that for two non asymptotic cases in which the signal to noise ratios snrs of the links satisfy certain conditions this scheme reduces to either df or cf for another set of non asymptotic snrs the generalized scheme is shown to yield strictly higher rates than both df and cf despite the complexity of the generalized scheme its rate advantage over df and cf is shown to be upper bounded by 0 5 bits per channel use this indicates that the practical benefit of the analysis of this scheme is to enable selecting the relaying mode that suits a given channel realization numerical results show that under rayleigh fading conditions this selection yields significant gains over fixed df and cf

multivariable fuzzy inference system for fingerprinting indoor localization the emergence of wireless sensor network has raised the need for cheap wireless indoor localization technique this paper considers the problem of fingerprinting indoor localization based on signal strength measurements rss a new approach based on fuzzy logic has been put forward the proposal makes use of k nearest neighbor classification in signal space the localization of target node is then determined as a weighted combination of nearest fingerprints the weights are determined using takagi sugeno fuzzy controller with two inputs a new enhancement to the knn is proposed to enhance the accuracy of location estimation this enhancement allows the knn to outlier some miss elected neighbors based on triangular area measurements the performance of the developed estimation algorithm has been evaluated using both monte carlo simulations and real testbed scenarios while compared to other alternative approaches

solving semidefinite quadratic linear programs using sdpt3 this paper discusses computational experiments with linear optimization problems involving semidefinite quadratic and linear cone constraints sqlps many test problems of this type are solved using a new release of sdpt3 a matlab implementation of infeasible primal dual path following algorithms the software developed by the authors uses mehrotra type predictor corrector variants of interior point methods and two types of search directions the hkm and nt directions a discussion of implementation details is provided and computational results on problems from the sdplib and dimacs challenge collections are reported

on instantaneous relaying the gaussian three node relay channel with orthogonal receive components i e the transmitted signals from the source and the relay do not interfere with each other is investigated for such channels linear relaying is a suboptimal strategy in general this is because a linear scheme merely repeats the received noisy signal and does not utilize the available degrees of freedom efficiently at this background nonlinear symbol wise as opposed to block wise relaying strategies are developed to compensate for the shortcomings of the linear strategy optimal strategies are presented for two special cases of the general scenario and it is shown that memoryless relaying can achieve the capacity furthermore for the general gaussian relay channel a parametric piecewise linear pl mapping is proposed and analyzed the achievable rates obtained by the pl mapping are computed numerically and optimized for a certain number of design parameters it is concluded that optimized pl relaying always outperforms conventional instantaneous linear relaying amplify and forward it is also illustrated that the proposed pl relaying scheme can improve on sophisticated block markov encoding i e decode and forward when the source relay link is ill conditioned relative to other links furthermore pl relaying can work at rates close to those achieved by side information encoding i e compress and forward but at a much lower complexity

functional equivalence between radial basis function networks and fuzzy inference systems it is shown that under some minor restrictions the functional behavior of radial basis function networks rbfns and that of fuzzy inference systems are actually equivalent this functional equivalence makes it possible to apply what has been discovered learning rule representational power etc for one of the models to the other and vice versa it is of interest to observe that two models stemming from different origins turn out to be functionally equivalent

fuzzy basis functions universal approximation and orthogonal least squares learning fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions using the stone weierstrass theorem it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy based on the fuzzy basis function representations an orthogonal least squares ols learning algorithm is developed for designing fuzzy systems based on given input output pairs then the ols algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system the fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system and the simulation results show that the control performance is improved by incorporating some common sense fuzzy control rules

high order fuzzy sliding manifold control the design of high order sliding fuzzy control strategies is addressed and carried out by using rigorous mathematical tools of the singular perturbation theory the resulting fuzzy controller implements an output feedback to solve output tracking problems and has the double benefit to be robust and low order namely the controller order is the plant relative degree simulations are presented to show the effectiveness of the proposed strategy

transmit signal and bandwidth optimization in multiple antenna relay channels in a multiple antenna relay channel the full duplex cut set capacity upper bound and decode and forward rate are formulated as convex optimization problems for half duplex relaying bandwidth allocation and transmit signals are optimized jointly moreover achievable rates based on the compress and forward strategy are presented using rate distortion and wyner ziv compression schemes

developing a local least squares support vector machines based neuro fuzzy model for nonlinear and chaotic time series prediction local modeling approaches owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models seem appealing for modeling identification and prediction applications in this paper we propose a local neuro fuzzy lnf approach based on the least squares support vector machines lssvms the proposed lnf approach employs lssvms which are powerful in modeling and predicting time series as local models and uses hierarchical binary tree hbt learning algorithm for fast and efficient estimation of its parameters the hbt algorithm heuristically partitions the input space into smaller subdomains by axis orthogonal splits in each partitioning the validity functions automatically form a unity partition and therefore normalization side effects e g reactivation are prevented integration of lssvms into the lnf network as local models along with the hbt learning algorithm yield a high performance approach for modeling and prediction of complex nonlinear time series the proposed approach is applied to modeling and predictions of different nonlinear and chaotic real world and hand designed systems and time series analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed lnf approach with the hbt learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series

a fuzzy approach to stability of fuzzy controllers this paper deals with stability analysis of fuzzy controllers a new definition embedding the notion of stability into fuzzy set theory is suggested in this context the introduction of a gradual stability is discussed investigating stability needs a model of the process one of the essential assumptions for the model used here is that the process has an approximatively linear structure within a sufficiently small time interval t possible deviations between the model and the real system are taken into account by specifying fuzzy parameters for the sake of simplicity however only a simple version of the model is considered in the present paper moreover some facts about the influence of the defuzzifier and the inference engine on the stability statement are presented by using this model and the suggested definition of stability some criteria ensuring stability are derived

the rate loss in the wyner ziv problem the rate distortion function for source coding with side information at the decoder the wyner ziv problem is given in terms of an auxiliary random variable which forms a markov chain with the source and the side information this markov chain structure typical to the solution of multiterminal source coding problems corresponds to a loss in coding rate with respect to the conditional rate distortion function i e to the case where the encoder is fully informed we show that for difference or balanced distortion measures this loss is bounded by a universal constant which is the minimax capacity of a suitable additive noise channel furthermore in the worst case this loss is equal to the maximin redundancy over the rate distortion function of the additive noise test channel for example the loss in the wyner ziv problem is less than 0 5 bit sample in the squared error distortion case and it is less than 0 22 bit for a binary source with hamming distance these results have implications also in universal quantization with side information and in more general multiterminal source coding problems

on channel capacity per unit cost memoryless communication channels with arbitrary alphabets where each input symbol is assigned a cost are considered the maximum number of bits that can be transmitted reliably through the channel per unit cost is studied it is shown that if the input alphabet contains a zero cost symbol then the capacity per unit cost admits a simple expression as the maximum normalized divergence between two conditional output distributions the direct part of this coding theorem admits a constructive proof via stein s lemma on the asymptotic error probability of binary hypothesis tests single user multiple access and interference channels are studied

threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec convolutional low density parity check ldpc ensembles introduced by felstrom and zigangirov have excellent thresholds and these thresholds are rapidly increasing functions of the average degree several variations on the basic theme have been proposed to date all of which share the good performance characteristics of convolutional ldpc ensembles we describe the fundamental mechanism that explains why convolutional like or spatially coupled codes perform so well in essence the spatial coupling of individual codes increases the belief propagation bp threshold of the new ensemble to its maximum possible value namely the maximum a posteriori map threshold of the underlying ensemble for this reason we call this phenomenon threshold saturation this gives an entirely new way of approaching capacity one significant advantage of this construction is that one can create capacity approaching ensembles with an error correcting radius that is increasing in the blocklength although we prove the threshold saturation only for a specific ensemble and for the binary erasure channel bec empirically the phenomenon occurs for a wide class of ensembles and channels more generally we conjecture that for a large range of graphical systems a similar saturation of the dynamical threshold occurs once individual components are coupled sufficiently strongly this might give rise to improved algorithms and new techniques for analysis

realization of pid controls by fuzzy control methods abstract this paper shows that pid controllers can be realized by fuzzy control methods of product sum gravity method and simplified fuzzy reasoning method pid controllers however cannot be constructed by min max gravity method known as mamdani s fuzzy reasoning method furthermore extrapolative reasoning can be executed by the product sum gravity method and simplified fuzzy reasoning method by extending membership functions of antecedent parts of fuzzy rules

capacity of a class of multicast tree networks in this paper we characterize the capacity of a new class of discrete memoryless multicast networks having a tree topology for achievability a novel coding scheme is constructed where some relays employ a combination of decode and forward and compress and forward and the other relays perform a random binning such that codebook constructions and relay operations are independent for each node and do not depend on the network topology for converse a new technique of iteratively manipulating inequalities exploiting the tree topology is used this class of multicast tree networks includes the class of diamond networks studied by kang and ulukus as a special case

new methodology for analytical and optimal design of fuzzy pid controllers describes a methodology for the systematic design of fuzzy pid controllers based on theoretical fuzzy analysis and genetic based optimization an important feature of the proposed controller is its simple structure it uses a one input fuzzy inference with three rules and at most six tuning parameters a closed form solution for the control action is defined in terms of the nonlinear tuning parameters the nonlinear proportional gain is explicitly derived in the error domain a conservative design strategy is proposed for realizing a guaranteed pid performance gpp fuzzy controller this strategy suggests that a fuzzy pid controller should be able to produce a linear function from its nonlinearity tuning of the system the proposed pid system is able to produce a close approximation of a linear function for approximating the gpp system this gpp system incorporated with a genetic solver for the optimization will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria two indexes linearity approximation index lai and nonlinearity variation index nvi are suggested for evaluating the nonlinear design of fuzzy controllers the proposed control system has been applied to several first order second order and fifth order processes simulation results show that the proposed fuzzy pid controller produces superior control performance to the conventional pid controllers particularly in handling nonlinearities due to time delay and saturation

a modified gradient based neuro fuzzy learning algorithm and its convergence neuro fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems in this paper a modified gradient based neuro fuzzy learning algorithm is proposed for zero order takagi sugeno inference systems this modified algorithm compared with conventional gradient based neuro fuzzy learning algorithm reduces the cost of calculating the gradient of the error function and improves the learning efficiency some weak and strong convergence results for this algorithm are proved indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value respectively a constant learning rate is used some conditions for the constant learning rate to guarantee the convergence are specified numerical examples are provided to support the theoretical findings

stable fuzzy logic design of point to point control for mechanical systems an approach for the development of fuzzy point to point control laws for second order mechanical systems is presented asymptotic stability of the resulting closed loop system is proved using lyapunov stability theory closed loop performance and robustness are quantified in terms of the parameters of membership functions as opposed to most existing fuzzy control laws the closed loop stability of the proposed controller does not depend on the knowledge of the entire dynamics moreover the approach does not require the plant to be open loop stable the proposed approach is demonstrated on design and simulation study of a fuzzy controller for a two link robotic arm

perturbation theory calculation of the pressure for an electron ion system abstract by means of finite temperature many body perturbation theory we derive through order e4 the corrections to an ideal fermi gas plus an ideal maxwell boltzmann gas of ions this computation is carried out for general values of the de broglie density the behavior of these coefficients is reported and their implications for the ionization profile at low densities are described

filter and forward relay design for mimo ofdm systems in this paper the filter and forward ff relay design for multiple input multiple output mimo orthogonal frequency division multiplexing ofdm systems is investigated due to the considered mimo structure the ff relay design is investigated in the framework of joint design together with the linear mimo transceiver as the design criterion first the minimization of weighted sum mean square error mse is considered the joint design in this case is approached based on alternating optimization that iterates between the optimal design of the ff relay for given mimo precoding and decoding matrices and the optimal design of mimo precoding and decoding matrices for a given ff relay filter second a more advanced problem of joint design for rate maximization is considered the second problem is approached based on the obtained result regarding the first problem of weighted sum mse minimization and the existing result regarding the relationship between weighted mse minimization and rate maximization numerical results show the effectiveness of the proposed ff relay design method and significant performance improvement by the proposed ff relay over widely considered simple af relays for mimo ofdm systems

an improvement of neuro fuzzy learning algorithm for tuning fuzzy rules based on the fuzzy clustering method we improved a neuro fuzzy learning algorithm in this improved approach before learning fuzzy rules we extract typical data from training data by using fuzzy c means clustering algorithm in order to remove redundant data and resolve conflicts in data and make them as practical training data by these typical data fuzzy rules can be tuned by using the neuro fuzzy learning algorithm therefore the learning time can be expected to be reduced and the fuzzy rules generated by the improved approach are reasonable and suitable for the identified system model moreover the efficiency of the improved method is also shown by identifying nonlinear functions

neuro fuzzy rule generation survey in soft computing framework the present article is a novel attempt in providing an exhaustive survey of neuro fuzzy rule generation algorithms rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network fuzzy sets are an aid in providing this information in a more human comprehensible or natural form and can handle uncertainties at various levels the neuro fuzzy approach symbiotically combining the merits of connectionist and fuzzy approaches constitutes a key component of soft computing at this stage to date there has been no detailed and integrated categorization of the various neuro fuzzy models used for rule generation we propose to bring these together under a unified soft computing framework moreover we include both rule extraction and rule refinement in the broader perspective of rule generation rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint models are grouped on the basis of their level of neuro fuzzy synthesis use of other soft computing tools like genetic algorithms and rough sets are emphasized rule generation from fuzzy knowledge based networks which initially encode some crude domain knowledge are found to result in more refined rules finally real life application to medical diagnosis is provided

asymptotic analysis of amplify and forward relaying in a parallel mimo relay network this paper studies the setup of a parallel mimo relay network in which k relays each equipped with n antennas assist in transmitting data between a source and a destination each equipped with m antennas m n in the half duplex mode it is assumed that there is no direct source destination link and the communication is performed in two hops an amplify and forward relaying scheme called incremental cooperative beamforming scheme icbs is introduced and shown to achieve the capacity of the network in the asymptotic case of k with a gap scaling of at most log k 4 k this result is shown to hold as long as the power of each relay is significantly larger than log3 k log log k k in addition in the case that the source power is equal to the relays power and both tend to infinity the proposed scheme is shown to achieve the full multiplexing gain regardless of the number of relays simulation results confirm the validity of analytical arguments

analytical structures for fuzzy pid controllers in this paper analytical structures for fuzzy proportional integral derivative pid controllers are derived by using triangular membership functions for inputs singletons or triangular membership functions for output minimum triangular norm maximum or drastic sum triangular conorm mamdani minimum drastic or larsen product inference nonlinear control rules and center of sum defuzzification it is shown that these analytical structures are not suitable for control purpose in this context it is extremely important to note that the analytical structures reported by carvajal et al are also not valid for control

polynomial based radial basis function neural networks p rbf nns realized with the aid of particle swarm optimization in this study we design polynomial based radial basis function neural networks p rbf nns based on a fuzzy inference mechanism the essential design parameters including learning rate momentum coefficient and fuzzification coefficient of the underlying clustering method are optimized by means of the particle swarm optimization the proposed p rbf nns dwell upon structural findings about training data that are expressed in terms of a partition matrix resulting from fuzzy clustering in this case being the fuzzy c means fcm the network is of functional nature as the weights between the hidden layer and the output are some polynomials the use of the polynomial weights becomes essential in capturing the nonlinear nature of data encountered in regression or classification problems from the perspective of linguistic interpretation the proposed network can be expressed as a collection of if then fuzzy rules the architecture of the networks discussed here embraces three functional modules reflecting the three phases of input output mapping realized in rule based architectures namely condition formation conclusion creation and aggregation the proposed classifier is applied to some synthetic and machine learning datasets and its results are compared with those reported in the previous studies

tsk fuzzy modeling based on spl epsiv insensitive learning in this paper new learning methods tolerant to imprecision are introduced and applied to fuzzy modeling based on the takagi sugeno kang fuzzy system the fuzzy modeling has an intrinsic inconsistency it may perform thinking tolerant to imprecision but learning methods are zero tolerant to imprecision the proposed methods make it possible to exclude this intrinsic inconsistency of a fuzzy modeling where zero tolerance learning is used to obtain fuzzy model tolerant to imprecision these new methods can be called spl epsiv insensitive learning or spl epsiv learning where in order to fit the fuzzy model to real data the spl epsiv insensitive loss function is used this leads to a weighted or fuzzified version of vapnik s support vector regression machine this paper introduces two approaches to solving the spl epsiv insensitive learning problem the first approach leads to the quadratic programming problem with bound constraints and one linear equality constraint the second approach leads to a problem of solving a system of linear inequalities two computationally efficient numerical methods for the spl epsiv insensitive learning are proposed the spl epsiv insensitive learning leads to a model with the minimal vapnik chervonenkis dimension which results in an improved generalization ability of this model and its outliers robustness finally numerical examples are given to demonstrate the validity of the introduced methods

fuzzy neural networks a survey abstract in this paper a fuzzy neural network will be a layered feedforward neural net that has fuzzy signals and or fuzzy weights we survey recent results on learning algorithms and applications for fuzzy neural networks

analysis of the convergence properties of topology preserving neural networks the authors provide a rigorous treatment of the convergence of the topology preserving neural networks proposed by kohonen for the one dimensional case the approach extends the original work by kohonen on the convergence properties of such networks in several respects first the authors investigate the convergence of the neuron weights directly as compared to kohonen s treatment of the dynamic behavior of the expectation values of the weights second the problem is formulated for a more general case of selecting the neighborhood amplitude of interaction rather than the uniform amplitude third the proof of convergence is based on the well known gladyshev theorem which uses lyapunov s function method the authors provide a step by step constructive proof which establishes the asymptotic convergence to a unique solution this proof also provides the relation between the boundary neurons weight vectors and the number of neurons in the network the approach is then extended to the two dimensional case and the result is stated in a theorem