combined fuzzy logic and genetic algorithm techniques application to an electromagnetic field problem the influence of a faulted electrical power transmission line on a buried pipeline is investigated the induced electromagnetic field depends on several parameters such as the position of the phase conductors the currents flowing through conducting materials and the earth resistivity a fuzzy logic system was used to simulate the problem it was trained using data derived from finite element method calculations for different configuration cases training set of the above electromagnetic field problem after the training the system was tested for several configuration cases differing significantly from the training cases with satisfactory results it is shown that the proposed method is very time efficient and accurate in calculating electromagnetic fields compared to the time straining finite element method in order to create the rule base for the fuzzy logic system a special incremental learning scheme is used during the training the system is trained using genetic algorithms binary and real genetic encoding were implemented and compared

numerical solution of systems of second order boundary value problems using continuous genetic algorithm in this paper continuous genetic algorithm is introduced as an efficient solver for systems of second order boundary value problems where smooth solution curves are used throughout the evolution of the algorithm to obtain the required nodal values of the unknown variables the solution methodology is based on representing each derivative in the system of differential equations by its finite difference approximation after that the overall residue for all nodes in the given system of differential equations is formulated the solution to the system of differential equations is then converted into the problem of minimizing the overall residue or maximizing the fitness function based on the nodal values generated from the genetic operators three numerical test problems including linear and nonlinear systems were analyzed to illustrate the procedure and confirm the performance of the proposed method in addition to that a convergence and sensitivity analysis to genetic operators and control parameters of the algorithm has been carried out the numerical results show that the proposed algorithm is a robust and accurate procedure for solving systems of second order boundary value problems furthermore the obtained accuracy for the solutions using cga is much better than the results obtained using some modern methods

inverse problem for multispecies ferromagneticlike mean field models in phase space with many states in this paper we solve the inverse problem for the curie weiss model and its multispecies version when multiple thermodynamic states are present as in the low temperature phase where the phase space is clustered the inverse problem consists of reconstructing the model parameters starting from configuration data generated according to the distribution of the model we demonstrate that without taking into account the presence of many states the application of the inversion procedure produces very poor inference results to overcome this problem we use the clustering algorithm when the system has two symmetric states of positive and negative magnetizations the parameter reconstruction can also be obtained with smaller computational effort simply by flipping the sign of the magnetizations from positive to negative or vice versa the parameter reconstruction fails when the system undergoes a phase transition in that case we give the correct inversion formulas for the curie weiss model and we show that they can be used to measure how close the system gets to being critical

a ga based fuzzy modeling approach for generating tsk models this paper proposes a new genetic based modeling method for building simple and well defined tsk models with scatter type input partitions our approach manages all attributes characterizing the structure of a tsk model simultaneously particularly it determines the number of rules the input partition the participating inputs in each rule and the consequent parameters the model building process is divided into two phases in phase one the structure learning task is formulated as a multi objective optimization problem which is resolved using a novel genetic based structure learning gbsl scheme apart from the mean square error mse and the number of rules three additional criteria are introduced in the fitness function for measuring the quality of the partitions optimization of these measures leads to models with representative rules small overlapping and efficient data cover in order to obtain models with accurate data fitting and good local performance the consequent parameters are determined using a local mse function while the overall model is evaluated on the basis of a global mse function the search capabilities of the suggested structure learning scheme are significantly enhanced by including a highly effective local search operator implemented by a micro genetic algorithm and four problem specific operators finally a genetic based parameter learning gbpl scheme is suggested in phase two which performs fine tuning of the initial models obtained after structure learning the performance of the proposed modeling approach is evaluated using a static example and a well known dynamic benchmark problem simulation results demonstrate that our models outperform those suggested by other methods with regard to simplicity model structure and accuracy

numerical solution of second order two point boundary value problems using continuous genetic algorithms summary second order two point boundary value problems are encountered in many engineering applications including the study of beam deflections heat flow and various dynamic systems two classical numerical techniques are widely used in the engineering community for the solution of such problems the shooting method and finite difference method these methods are suited for linear problems however when solving the non linear problems these methods require some major modifications that include the use of some root finding technique furthermore they require the use of other basic numerical techniques in order to obtain the solution in this paper the author introduces a novel method based on continuous genetic algorithms for numerically approximating a solution to this problem the new method has the following characteristics first it does not require any modification while switching from the linear to the non linear case as a result it is of versatile nature second this approach does not resort to more advanced mathematical tools and is thus easily accepted in the engineering application field third the proposed methodology has an implicit parallel nature which points to its implementation on parallel machines however being a variant of the finite difference scheme with truncation error of the order o h 2 the method provides solutions with moderate accuracy numerical examples presented in the paper illustrate the applicability and generality of the proposed method copyright 2004 john wiley sons ltd

data driven fuzzy modeling for takagi sugeno kang fuzzy system this paper presents a systematic approach to design first order tagaki sugeno kang tsk fuzzy systems this approach attempts to obtain the fuzzy rules without any assumption about the structure of the data the structure identification and parameter optimization steps in this approach are carried out automatically and are capable of finding the optimal number of the rules with an acceptable accuracy starting with an initial structure the system first tries to improve the structure and then as soon as an improved structure is found it fine tunes its rules parameters then it goes back to improve the structure again to find a better structure and re fine tune the rules parameters this loop continues until a satisfactory solution tsk model is found the proposed approach has successfully been applied to well known benchmark datasets and real world problems the obtained results are compared with those obtained with other methods from the literature experimental studies demonstrate that the predicted properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules finally as a case study the proposed approach is applied to the desulfurization process of a real steel industry comparing the proposed approach with some other fuzzy systems and neural networks it is shown that the developed tsk fuzzy system exhibits better results with higher accuracy and smaller size of architecture

selecting fuzzy if then rules for classification problems using genetic algorithms this paper proposes a genetic algorithm based method for selecting a small number of significant fuzzy if then rules to construct a compact fuzzy classification system with high classification power the rule selection problem is formulated as a combinatorial optimization problem with two objectives to maximize the number of correctly classified patterns and to minimize the number of fuzzy if then rules genetic algorithms are applied to this problem a set of fuzzy if then rules is coded into a string and treated as an individual in genetic algorithms the fitness of each individual is specified by the two objectives in the combinatorial optimization problem the performance of the proposed method for training data and test data is examined by computer simulations on the iris data of fisher

identifying a fuzzy model by using the bipartite membership functions the gradient descent method and genetic algorithms have been widely used to refine fuzzy models constructed for the given data this paper approaches from another viewpoint to adjusting the fuzzy model built for the preprocessed data the membership functions defined for each premise variable are equally distributed and fixed in the transformed domain to better identify the fuzzy model either the transformation functions or consequent parts of fuzzy rules or both need to be optimized since adjusting a rule to satisfy one pattern may deteriorate the others performance and result in a lengthy tuning process we then treat each triangular membership function as two disjoint ones such that each fuzzy rule is divided into mutually independent rules this in turn benefits the refinement of consequent parts in the fuzzy rules since adjusting a rule will not affect the others not only the simulation results from the proposed model will be demonstrated but also the results from the conventional approaches will be given for comparisons we use the least squared method to calculate the desired consequent real numbers for the data located in the same region of transformed domain the conformity of the after tuned consequent parts in the fuzzy rules with the desired values further verifies the effectiveness of the presented methodology

completeness and consistency conditions for learning fuzzy rules the completeness and consistency conditions were introduced in order to achieve acceptable concept recognition rules in real problems we can handle noise affected examples and it is not always possible to maintain both conditions moreover when we use fuzzy information there is a partial matching between examples and rules therefore the consistency condition becomes a matter of degree in this paper a learning algorithm based on soft consistency and completeness conditions is proposed this learning algorithm combines in a single process rule and feature selection and it is tested on different databases

linear physical layer network coding over hybrid finite ring for rayleigh fading two way relay channels in this paper we propose a novel linear physical layer network coding scheme over hybrid finite ring hfr lpnc for rayleigh fading two way relay channels the relay maps the superimposed signal of the two users to a linear network coded combination lncc in hybrid finite ring rather than using the simple bit wise exclusive or mapping the optimal linear coefficients are selected to generate the lncc aiming to 1 max imize the sum rate in the mac phase and 2 ensure unambigu ous decoding to avoid the performance degradation caused by high order irregular mappings properly designed source coding is used for compressing the lncc alphabet over the hybrid finite ring into the unifying 4 ary alphabet we derive the constella tion constrained sum rates for hfr lpnc in comparison with 5qam denoise and forward 5qam dnf which we use as a reference scheme furthermore we explicitly characterize the rate difference between hfr lpnc and 5qam dnf our analysis and simulation show that 1 hfr lpnc has a superior ability to mitigate the singular fading compared with 5qam dnf and 2 hfr lpnc is superior to 5qam dnf over a wide range of snrs

simplifying fuzzy modeling by both gray relational analysis and data transformation methods abstract instead of following the traditional approaches which utilize original data patterns to construct the fuzzy model this paper proposes to exploit both gray relational analysis and data transformation techniques to simplify the modeling procedures the transformation method allows us to map the original data to other domains such that there is no need to adjust the membership functions and the fuzzification process is simply taking place on the fixed ones since too many system variables involved may complicate the fuzzy modeling the gray relational method is exploited to select the crucial variables from a finite set of candidates based on the calculated relational degrees between the output and the prospective input variables we can decide which are the important premise variables the proposed methods have definite effects on the model s performance therefore the way to systematically adjust the transformation functions is also investigated ease in selecting the premise variables and minimal effort needed to adjust system parameters are the merits of the proposed work simulation results from two different examples are presented to demonstrate the superiority of the proposed model to the conventional methodologies

joint physical layer coding and network coding for bidirectional relaying we consider a communication system where two transmitters wish to exchange information through a central relay the transmitter and relay nodes exchange data over synchronized average power constrained additive white gaussian noise channels with a real input with signal to noise ratio snr of snr an upper bound on the capacity is 1 2 log 1 snr bits per transmitter per use of the multiple access phase and broadcast phase of the bidirectional relay channel we show that using lattice codes and lattice decoding we can obtain a rate of 1 2 log 1 2 snr bits per transmitter which is essentially optimal at high snr the main idea is to decode the sum of the codewords modulo a lattice at the relay followed by a broadcast phase which performs slepian wolf coding we also show that if the two transmitters use identical lattices with minimum angle decoding we can achieve the same rate of 1 2 log 1 2 snr the proposed scheme can be thought of as a joint physical layer network layer code which outperforms other recently proposed analog network coding schemes

ga based fuzzy neural controller design for municipal incinerators the successful operation of mass burn municipal incinerators for solid waste management involves many uncertain factors not only the physical composition and chemical property of waste streams but also the complexity of combustion mechanism would significantly influence the performance of waste incineration due to the rising concerns of dioxin furan emissions from those incineration facilities in the metropolitan region the applications of fuzzy control technologies for reducing the operational risks have gradually received wide attention in the scientific community recent advances of intelligent combustion control icc technologies indicate that hybrid fuzzy control schemes integrating some ideas and paradigms existing in different soft computing approaches may provide more reliable control performance of combustion process the proposed integrated methodology using genetic algorithms and neural networks as tools to aid in fuzzy logic control therefore employs a three stage analysis it applies three soft computing approaches simultaneously for generating a representative state function searching for a set of multi objective control strategies and auto tuning the fuzzy control rule base for use in the case study the findings from this research clearly indicate that the control performance at least in three types of municipal incinerators i e patented stokers are takuma volund and martin is greatly enhanced via the use of icc technologies by using this integrated methodology the case study not only verifies the applicability and suitability for controlling municipal incinerators but also presents application potentials for controlling many other types of industrial incinerators such as modular rotary kiln and fluidized bed incinerators

fuzzy connectives based crossover operators to model genetic algorithms population diversity abstract genetic algorithms are adaptive methods which may be used to solve search and optimization problems genetic algorithms process a population of search space solutions with three operations selection crossover and mutation an important problem in the use of genetic algorithms is the premature convergence in a local optimum their main causes are the lack of diversity in the population and the disproportionate relationship between exploitation and exploration the crossover operator is considered one of the most determinant elements for solving this problem in this paper we present new crossover operators based on fuzzy connectives for real coded genetic algorithms these operators are designed to avoid the premature convergence problem to do so they should keep the right exploitation exploration balance to suitably model the diversity of the population

applicability of the fuzzy operators in the design of fuzzy logic controllers a study of the different roles played by the fuzzy operators in fuzzy control is developed in this paper the behavior of a very large amount of fuzzy operators is analyzed and a comparison of the accuracy of many fuzzy logic controllers designed by means of these operators is carried out in order to do that a comparison methodology is defined and two fuzzy control applications are selected the inverted pendulum problem and the fuzzy modeling of the real curve y x

fuzzy modelling and identification with genetic algorithm based learning abstract a ga based learning algorithm is proposed in this paper for the identification of tsk models the algorithm consists of four blocks partition block ga block tuning block and termination block the partition block is to determine an estimated partition of input variables the ga block is to optimise the structure of a tsk model the tuning block is to fine tune the parameters of the tsk model using the gradient descent based approach and the termination block checks that the resultant tsk model is satisfactory the proposed gabl algorithm has the advantage of simplicity flexibility high accuracy and automation the presented numerical examples indicate that the gabl algorithm is effective in constructing a good tsk model for complex nonlinear systems

the degrees of freedom of compute and forward we analyze the asymptotic behavior of compute and forward relay networks in the regime of high signal to noise ratios we consider a section of such a network consisting of k transmitters and k relays the aim of the relays is to reliably decode an invertible function of the messages sent by the transmitters an upper bound on the capacity of this system can be obtained by allowing full cooperation among the transmitters and among the relays transforming the network into a k k multiple input multiple output mimo channel the number of degrees of freedom of compute and forward is hence at most k in this paper we analyze the degrees of freedom achieved by the lattice coding implementation of compute and forward proposed recently by nazer and gastpar we show that this lattice implementation achieves at most 2 1 1 k 2 degrees of freedom thus exhibiting a very different asymptotic behavior than the mimo upper bound this raises the question if this gap of the lattice implementation to the mimo upper bound is inherent to compute and forward in general we answer this question in the negative by proposing a novel compute and forward implementation achieving k degrees of freedom

diophantine approximation and applications in interference alignment abstract this paper is motivated by recent applications of diophantine approximation in electronics in particular in the rapidly developing area of interference alignment some remarkable advances in this area give substantial credit to the fundamental khintchine groshev theorem and in particular to its far reaching generalisation for submanifolds of a euclidean space with a view towards the aforementioned applications here we introduce and prove quantitative explicit generalisations of the khintchine groshev theorem for non degenerate submanifolds of r n the importance of such quantitative statements is explicitly discussed in jafar s monograph 12 4 7 1

self tuning fuzzy modeling with adaptive membership function rules and hierarchical structure based on genetic algorithm abstract recently fuzzy systems have been used in many fields and places in order to apply the fuzzy system to the various fields the tuning and optimizing method of the fuzzy system is the key issue some self tuning methods have been proposed so far however these conventional self tuning methods do not have sufficient capability of learning in this paper we propose a new supervised self tuning fuzzy modeling which consist of some membership function expressed by the radial basis function with insensitive region learning is carried out by the genetic algorithms the descent method is also utilized for tuning the shapes of the membership function and consequent parts the effectiveness of the proposed methods is shown by some numerical examples

online data driven fuzzy clustering with applications to real time robotic tracking robotic target tracking has been used in a variety of applications due to limited sampling rate sensory characteristics and processing delays an important issue in such systems is to extrapolate ahead the trajectory position orientation velocity and or acceleration of moving targets from past observations this paper introduces a novel online data driven fuzzy clustering algorithm that is based on the maximum entropy principle for this particular task in this algorithm the fuzzy inference mechanism is extracted automatically from observed data without human help which thus eliminates the necessity of expert s knowledge and a priori information on moving targets as required by most traditional techniques this algorithm does not require training which enables it to work in a completely online fashion another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time in the meantime the introduced algorithm is able to adapt quickly to the dynamics of moving targets all these desired features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking simulation results show the effectiveness and efficiency of the presented algorithm

computation alignment capacity approximation without noise accumulation consider several source nodes communicating across a wireless network to a destination node with the help of several layers of relay nodes recent work by avestimehr has approximated the capacity of this network up to an additive gap the communication scheme achieving this capacity approximation is based on compress and forward resulting in noise accumulation as the messages traverse the network as a consequence the approximation gap increases linearly with the network depth this paper develops a computation alignment strategy that can approach the capacity of a class of layered time varying wireless relay networks up to an approximation gap that is independent of the network depth this strategy is based on the compute and forward framework which enables relays to decode deterministic functions of the transmitted messages alone compute and forward is insufficient to approach the capacity as it incurs a penalty for approximating the wireless channel with complex valued coefficients by a channel with integer coefficients here this penalty is circumvented by carefully matching channel realizations across time slots to create integer valued effective channels that are well suited to compute and forward unlike prior constant gap results the approximation gap obtained in this paper also depends closely on the fading statistics which are assumed to be i i d rayleigh

compute compress and forward exploiting asymmetry of wireless relay networks compute and forward cf harnesses interference in a wireless network by allowing relays to compute combinations of source messages the computed message combinations at relays are correlated and so directly forwarding these combinations to a destination generally incurs information redundancy and spectrum inefficiency to address this issue we propose a novel relay strategy termed compute compress and forward ccf in ccf source messages are encoded using nested lattice codes constructed on a chain of nested coding and shaping lattices a key difference of ccf from cf is an extra compressing stage inserted in between the computing and forwarding stages of a relay so as to reduce the forwarding information rate of the relay the compressing stage at each relay consists of two operations first to quantize the computed message combination on an appropriately chosen lattice referred to as a quantization lattice and then to take modulo on another lattice referred to as a modulo lattice we study the design of the quantization and modulo lattices and propose successive recovering algorithms to ensure the recoverability of source messages at destination based on that we formulate a sum rate maximization problem that is in general an np hard mixed integer program a low complexity algorithm is proposed to give a suboptimal solution numerical results are presented to demonstrate the superiority of ccf over the existing cf schemes

stability issues on takagi sugeno fuzzy model parametric approach the main objective of this research is to provide a systematic stability analysis technique for fuzzy systems using well established results from robust stability of a family of polynomials the model based takagi sugeno 1985 fuzzy control system is considered and shown to have a dependent coefficient structure involving firing strengths two kharitonov regions via overbounding are established to facilitate the extreme results for stability test in the space of polynomials as well as the complex plane the criterion obtained is a sufficient condition for a fuzzy logic controller three nonlinear examples are demonstrated to show that the systematic approach proposed here can be used to solve fuzzy stability problems fuzzy users concerned over stability issues may find these analytic tools useful

universal approximation by hierarchical fuzzy system with constraints on the fuzzy rule this paper presents a special hierarchical fuzzy system where the outputs of the previous layer are not used in the if parts but used only in the then parts of the fuzzy rules of the current layer the proposed scheme can be shown to be a universal approximator to any continuous function on a compact set if complete fuzzy sets are used in the if parts of the fuzzy rules with singleton fuzzifier and center average defuzzifier from the simulation of ball and beam control system it is demonstrated that the proposed scheme approximates with good accuracy the model nonlinear controller with fewer fuzzy rules than the centralized fuzzy system and its control performance is comparable to that of the nonlinear controller

inverse spin glass and related maximum entropy problems if we have a system of binary variables and we measure the pairwise correlations among these variables then the least structured or maximum entropy model for their joint distribution is an ising model with pairwise interactions among the spins here we consider inhomogeneous systems in which we constrain for example not the full matrix of correlations but only the distribution from which these correlations are drawn in this sense what we have constructed is an inverse spin glass rather than choosing coupling constants at random from a distribution and calculating correlations we choose the correlations from a distribution and infer the coupling constants we argue that such models generate a block structure in the space of couplings which provides an explicit solution of the inverse problem this allows us to generate a phase diagram in the space of measurable moments of the distribution of correlations we expect that these ideas will be most useful in building models for systems that are nonequilibrium statistical mechanics problems such as networks of real neurons

compute and forward harnessing interference through structured codes interference is usually viewed as an obstacle to communication in wireless networks this paper proposes a new strategy compute and forward that exploits interference to obtain significantly higher rates between users in a network the key idea is that relays should decode linear functions of transmitted messages according to their observed channel coefficients rather than ignoring the interference as noise after decoding these linear equations the relays simply send them towards the destinations which given enough equations can recover their desired messages the underlying codes are based on nested lattices whose algebraic structure ensures that integer combinations of codewords can be decoded reliably encoders map messages from a finite field to a lattice and decoders recover equations of lattice points which are then mapped back to equations over the finite field this scheme is applicable even if the transmitters lack channel state information

asafes2 a novel neuro fuzzy architecture for fuzzy computing based on functional reasoning abstract the functional reasoning or takagi sugeno fuzzy reasoning method is a very promising approach to the task of multi variable non linear function approximation and the design of a fuzzy model and fuzzy controller the proposed architecture asafes2 is a neuro fuzzy function approximator and is the first attempt to combine this reasoning method with stochastic reinforcement learning it can learn a function to a high degree of accuracy at a very great speed and furthermore expresses it in a set of linear relations the main ideas are the fuzzy partitioning of the input space into fuzzy subspaces each corresponding to a possible fuzzy rule and the use of a separate neural unit for every fuzzy subspace in order to calculate the optimum consequence parameters simulation results prove its superiority over back propagation in simple non linear function approximation tasks and over all previous approaches to the box and jenkins gas furnace modelling comparisons are also made with generalized radial basis function networks finally asafes2 as a building block in a learning fuzzy logic controller is applied to the well known cart pole stabilization problem with satisfactory results a new flexible membership function is also presented

structured lattice codes for some two user gaussian networks with cognition coordination and two hops we study a number of two user interference networks with multiple antenna transmitters receivers mimo transmitter side information in the form of linear combinations over an appropriate finite field of the information messages and two hop relaying we start with a cognitive interference channel cic where one of the transmitters noncognitive has knowledge of a rank 1 linear combination of the two information messages while the other transmitter cognitive has access to a rank 2 linear combination of the same messages this is referred to as the network coded cic since such linear combination may be the consequence of some random linear network coding scheme implemented in the backbone wired network for such channel we develop an achievable region based on a few novel concepts precoded compute and forward pcof with channel integer alignment cia combined with standard dirty paper coding we also develop a capacity region outer bound and find the symmetric generalized degrees of freedom gdof region of the network coded cic through the gdof characterization we show that knowing mixed data linear combinations of the information messages provides a unbounded spectral efficiency gain over the classical cic counterpart if the ratio in decibel of signal to noise snr to interference to noise is larger than certain threshold then we consider a gaussian relay network having the two user mimo ic as the main building block we use pcof with cia to convert the mimo ic into a deterministic finite field ic then we use a linear precoding scheme over the finite field to eliminate interference in the finite field domain using this unified approach we derive the symmetric sum rate of the two user mimo ic with coordination cognition and two hops we also provide finite snr results not just dof which show that the proposed coding schemes are competitive against state of the art interference avoidance scheme based on orthogonal access for standard randomly generated rayleigh fading channels

a method of converting a fuzzy system to a two layered hierarchical fuzzy system and its run time efficiency in classical single layer fuzzy systems fss the number of rules and the run time computational requirements increase exponentially with the number of input domains in this paper we present a method for converting a multidimensional fs to a two layer hierarchical fs that reduces the number of rules and improves the run time efficiency the first layer of the two layer system consists of fss whose rule bases can be represented as linearly independent vectors the second layer constructs linear combinations of the rule base vectors the effectiveness of the hierarchical conversion in reducing the number of rules and improving efficiency is demonstrated on a classic control problem and a simulated higher dimensional fs

the degrees of freedom of the k user gaussian interference channel is discontinuous at rational channel coefficients the degrees of freedom of a k user gaussian interference channel gic has been defined to be the multiple of 1 2 log 2 p at which the maximum sum of achievable rates grows with increasing power p in this paper we establish that the degrees of freedom of three or more user real scalar gics viewed as a function of the channel coefficients is discontinuous at points where all of the coefficients are nonzero rational numbers more specifically for all k 2 we find a class of k user gics that is dense in the gic parameter space for which k 2 degrees of freedom are exactly achievable and we show that the degrees of freedom for any gic with nonzero rational coefficients is strictly smaller than k 2 these results are proved using new connections with number theory and additive combinatorics

hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule based systems genetic algorithms and evolution strategies are combined in order to build a multi stage hybrid evolutionary algorithm for learning constrained approximate mamdani type knowledge bases from examples the genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule based systems the proposed genetic fuzzy rule based system is used to solve an electrical engineering problem and the results obtained are compared with other methods presenting different characteristics

a systematic approach for designing multistage fuzzy control systems this paper proposes a systematic approach for designing a multistage fuzzy logic controller mflc for large scale nonlinear systems in designing such a controller the major tasks are to derive fuzzy rule bases determine membership functions of input output variables and design input output scaling factors in this work the fuzzy rule bases are generated by rule generated functions which are based on the negative gradient of a system performance index the membership functions of isosceles triangle of input output variables are fixed in the same cardinality and only the input output scaling factors are optimized from a genetic algorithm based on a fitness function as a result the search space of the parameters is narrowed down to a small space so that the mflc can be quickly constructed and the fuzzy rules and scaling factors can easily be determined the performance of the proposed approach is examined by computer simulations on an inverted pendulum system the performance of single stage structure binary tree structure and skew binary tree structure are compared the binary tree structure has better performance and use fewer fuzzy rules in the illustrative example

modeling of hierarchical fuzzy systems a new kind of mapping rule base scheme is proposed to get the fuzzy rules of hierarchical fuzzy systems the algorithm of this scheme is developed such that one can easily design the involved fuzzy rules in the middle layers of the hierarchical structure in contrast with the conventional single layer fuzzy controller the present method has approximate performance using the same scaling factors next examples are given at last simulated results demonstrate that the algorithm is effective and feasible

interference alignment from degrees of freedom to constant gap capacity approximations interference alignment is a key technique for communication scenarios with multiple interfering links in several such scenarios interference alignment was used to characterize the degrees of freedom of the channel however these degree of freedom capacity approximations are often too weak to make accurate predictions about the behavior of channel capacity at finite signal to noise ratios snrs the aim of this paper is to significantly strengthen these results by showing that interference alignment can be used to characterize capacity to within a constant gap we focus on real time invariant frequency flat x channels the only known solutions achieving the degrees of freedom of this channel are either based on real interference alignment or on layer selection schemes neither of these solutions seems sufficient for a constant gap capacity approximation in this paper we propose a new communication scheme and show that it achieves the capacity of the gaussian x channel to within a constant gap to aid in this process we develop a novel deterministic channel model this deterministic model depends on the 1 2 log snr most significant bits of the channel coefficients rather than only the single most significant bit used in conventional deterministic models the proposed deterministic model admits a wider range of achievable schemes that can be translated to the gaussian channel for this deterministic model we find an approximately optimal communication scheme we then translate this scheme for the deterministic channel to the original gaussian x channel and show that it achieves capacity to within a constant gap this is the first constant gap result for a general fully connected network requiring interference alignment

virtual full duplex relaying with half duplex relays we consider virtual full duplex relaying by means of half duplex relays in this configuration each relay stage in a multihop relaying network is formed by at least two relays used alternatively in transmit and receive modes such that while one relay transmits its signal to the next stage the other relay receives a signal from the previous stage with such a pipelined scheme the source is active and sends a new information message in each time slot we consider the achievable rates for various coding schemes and compare them with a cut set upper bound which is tight in certain conditions we show that both lattice based compute and forward cof and quantize map and forward qmf yield attractive performance and implementation in particular qmf in this context does not require long messages and joint non unique decoding if the quantization mean square distortion at the relays is chosen appropriately in addition in the multihop case the gap of qmf from the cut set upper bound grows logarithmically with the number of stages and not linearly as in the case of noise level quantization furthermore we show that cof is particularly attractive in the case of multihop relaying when the channel gains have fluctuations not larger than 3 db yielding a rate that does not depend on the number of relaying stages in particular we argue that such architecture may be useful for a wireless backhaul with line of sight propagation between the relays

achieving 1 2 log 1 snr on the awgn channel with lattice encoding and decoding we address an open question regarding whether a lattice code with lattice decoding as opposed to maximum likelihood ml decoding can achieve the additive white gaussian noise awgn channel capacity we first demonstrate how minimum mean square error mmse scaling along with dithering lattice randomization techniques can transform the power constrained awgn channel into a modulo lattice additive noise channel whose effective noise is reduced by a factor of spl radic 1 snr snr for the resulting channel a uniform input maximizes mutual information which in the limit of large lattice dimension becomes 1 2 log 1 snr i e the full capacity of the original power constrained awgn channel we then show that capacity may also be achieved using nested lattice codes the coarse lattice serving for shaping via the modulo lattice transformation the fine lattice for channel coding we show that such pairs exist for any desired nesting ratio i e for any signal to noise ratio snr furthermore for the modulo lattice additive noise channel lattice decoding is optimal finally we show that the error exponent of the proposed scheme is lower bounded by the poltyrev exponent

thermodynamics and signatures of criticality in a network of neurons the activity of a neural network is defined by patterns of spiking and silence from the individual neurons because spikes are relatively sparse patterns of activity with increasing numbers of spikes are less probable but with more spikes the number of possible patterns increases this tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics we construct this relationship for populations of up to n 160 neurons in a small patch of the vertebrate retina using a combination of direct and model based analyses of experiments on the response of this network to naturalistic movies we see signs of a thermodynamic limit where the entropy per neuron approaches a smooth function of the energy per neuron as n increases the form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point we suggest further tests of criticality and give a brief discussion of its functional significance

fuzzy model predictive control of non linear processes using genetic algorithms abstract this paper introduces a new fuzzy control technique which belongs to the popular family of control algorithms called model predictive controllers the method is based on a dynamic fuzzy model of the process to be controlled which is used for predicting the future behavior of the output variables a non linear optimization problem is then formulated which minimizes the difference between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon the problem is solved on line using a specially designed genetic algorithm which has a number of advantages over conventional non linear optimization techniques the method can be used with any type of fuzzy model and is particularly useful when a direct fuzzy controller cannot be designed due to the complexity of the process and the difficulty in developing fuzzy control rules the method is illustrated via the application to a non linear single input single output reactor where a takagi sugeno model serves as a predictor of the process future behavior

blind compute and forward compute and forward c f is a promising new approach to interference management enjoying several advantages over other information theoretic schemes c f usually requires channel state information csi at the receivers so that an optimal scaling factor can be computed for the purposes of decoding in this paper a blind c f scheme i e one not requiring csi is developed rather than attempting to compute the optimal scaling factor this new scheme seeks one or more good scalars i e scalars that allow correct decoding despite possibly being suboptimal the region of all such good scalars is characterized to find a good scalar a computationally efficient scheme is proposed which involves error detection a hierarchically organized list as well as a use of the smoothing lemma from lattice theory simulation results show that our blind c f scheme achieves for a class of nested lattice codes the same throughput as its csi enabled counterpart at the expense of approximately a two fold increase in computational complexity in the high throughput region moreover our blind c f scheme can be applied to multisource multirelay networks with a good performance complexity tradeoff

automatic design of hierarchical takagi sugeno type fuzzy systems using evolutionary algorithms this paper presents an automatic way of evolving hierarchical takagi sugeno fuzzy systems ts fs the hierarchical structure is evolved using probabilistic incremental program evolution pipe with specific instructions the fine tuning of the if then rule s parameters encoded in the structure is accomplished using evolutionary programming ep the proposed method interleaves both pipe and ep optimizations starting with random structures and rules parameters it first tries to improve the hierarchical structure and then as soon as an improved structure is found it further fine tunes the rules parameters it then goes back to improve the structure and the rules parameters this loop continues until a satisfactory solution hierarchical ts fs model is found or a time limit is reached the proposed hierarchical ts fs is evaluated using some well known benchmark applications namely identification of nonlinear systems prediction of the mackey glass chaotic time series and some classification problems when compared to other neural networks and fuzzy systems the developed hierarchical ts fs exhibits competing results with high accuracy and smaller size of hierarchical architecture

the approximate sum capacity of the symmetric gaussian k user interference channel interference alignment has emerged as a powerful tool in the analysis of multiuser networks despite considerable recent progress the capacity region of the gaussian k user interference channel is still unknown in general in part due to the challenges associated with alignment on the signal scale using lattice codes this paper develops a new framework for lattice interference alignment based on the compute and forward approach within this framework each receiver decodes by first recovering two or more linear combinations of the transmitted codewords with integer valued coefficients and then solving these linear combinations for its desired codeword for the special case of symmetric channel gains this framework is used to derive the approximate sum capacity of the gaussian interference channel up to an explicitly defined outage set of the channel gains the key contributions are the capacity lower bounds for the weak through strong interference regimes where each receiver should jointly decode its own codeword along with part of the interfering codewords as part of the analysis it is shown that decoding k linear combinations of the codewords can approach the sum capacity of the k user gaussian multiple access channel up to a gap of no more than k 2 log k bits

a class of hierarchical fuzzy systems with constraints on the fuzzy rules this paper presents a class of hierarchical fuzzy systems where previous layer outputs are used not in if parts but only in then parts of the fuzzy rules of the current layer the proposed scheme is shown to be a universal approximator to any real continuous function on a compact set if complete fuzzy sets are used in the if parts of the fuzzy rules with singleton fuzzifier and center average defuzzifier from the example of ball and beam control system simulation it is demonstrated that the proposed scheme approximates with high accuracy a model nonlinear controller with fewer fuzzy rules than a centralized fuzzy system and its control performance is comparable to that of a nonlinear controller

a methodology for fuzzy modeling of engineering systems abstract this paper describes a systematic approach to the modeling of engineering systems using a fuzzy formulation that is independent of human knowledge the computer algorithm described here operates on a set of experimental observations of the system and constructs an optimum fuzzy model for these observations the program automatically selects membership functions deduces inference rules constructs logical relations and determines the formulae for conducting union and intersection operations membership functions rules and logical operations are defined parametrically model parameters are optimized so that the model can at least re produce with minimum error the data that were used in obtaining the membership functions and rules therefore model parameters are optimized to minimize error or entropy of the back inferences of the observations from which the model was constructed to reach the global minimum and avoid entrapment in a local minimum a random search is carried out then followed by a systematic hooke jeeves search optimization algorithm it has been found that this technique is more successful compared with other statistical techniques

lattices over eisenstein integers for compute and forward in this paper we consider the use of lattice codes over eisenstein integers for implementing a compute and forward protocol in wireless networks when channel state information is not available at the transmitter we extend the compute and forward paradigm of nazer and gastpar to decoding eisenstein integer combinations of transmitted messages at relays by proving the existence of a sequence of pairs of nested lattices over eisenstein integers in which the coarse lattice is good for covering and the fine lattice can achieve the poltyrev limit using this result we show that both the outage performance and error correcting performance of the nested lattice codebooks over eisenstein integers surpass those of lattice codebooks over integers considered by nazer and gastpar with no additional computational complexity

averaging bounds for lattices and linear codes general random coding theorems for lattices are derived from the minkowski hlawka theorem and their close relation to standard averaging arguments for linear codes over finite fields is pointed out a new version of the minkowski hlawka theorem itself is obtained as the limit for p spl rarr spl infin of a simple lemma for linear codes over gf p used with p level amplitude modulation the relation between the combinatorial packing of solid bodies and the information theoretic soft packing with arbitrarily small but positive overlap is illuminated the soft packing results are new when specialized to the additive white gaussian noise channel they reduce to a version of the de buda poltyrev result that spherically shaped lattice codes and a decoder that is unaware of the shaping can achieve the rate 1 2 log sub 2 p n