achievable performance limitations for siso plants with pole location constraints in this paper performance limitations in the linear control of general linear discrete time scalar systems are considered the performance is measured with the 2 norm of an error function which at the same time ensures that the closed loop poles lie in a prescribed region the deleterious effect of this constraint on a classical performance measurement is also quantified in such a way that the interplay between the size of the desired region and structural features of the plant is revealed

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

space time block codes from orthogonal designs we introduce space time block coding a new paradigm for communication over rayleigh fading channels using multiple transmit antennas data is encoded using a space time block code and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas the received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise maximum likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection this uses the orthogonal structure of the space time block code and gives a maximum likelihood decoding algorithm which is based only on linear processing at the receiver space time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm the classical mathematical framework of orthogonal designs is applied to construct space time block codes it is shown that space time block codes constructed in this way only exist for few sporadic values of n subsequently a generalization of orthogonal designs is shown to provide space time block codes for both real and complex constellations for any number of transmit antennas these codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as pam for an arbitrary complex constellation such as psk and qam space time block codes are designed that achieve 1 2 of the maximum possible transmission rate for any number of transmit antennas for the specific cases of two three and four transmit antennas space time block codes are designed that achieve respectively all 3 4 and 3 4 of maximum possible transmission rate using arbitrary complex constellations the best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well

mimo interaction measure and controller structure selection in this paper a mimo interaction measure is described and its use in structure selection of multivariable controllers is discussed the proposal is built upon a gramian based measure of interaction in multivariable plants and the results in this paper apply to stable processes this measure provides support for decentralized input output pairing as well as for a richer controller architecture selection in continuous and discrete time time frameworks including triangular block diagonal and sparse structures

mixed h 2 h autopilot design of bank to turn missiles using fuzzy basis function networks this paper presents a novel mixed h2 h autopilot design for bank to turn missiles based on sliding control theory the proposed autopilot utilizes a fuzzy basis function network to approximate the unknown nonlinear functions of bank to turn missile dynamics enabling a mixed h2 h controller with weight updating laws to be applied to attenuate the effects of approximation errors and external disturbances moreover the mixed h2 h autopilot design can minimize h2 performance criteria under a desired h disturbance rejection constraint unlike general mixed h2 h control problems the proposed h2 h autopilot with self tuning fuzzy basis function network can avoid solving coupled riccati equations by adequately selecting diagonal weighting matrices in the h2 performance criterion and h constraint therefore the proposed h2 h control approach can be implemented efficiently computer simulation results are presented to illustrate the effectiveness of the proposed mixed h2 h autopilot for btt missiles

computing derivatives in interval type 2 fuzzy logic systems this paper makes type 2 fuzzy logic systems much more accessible to fuzzy logic system designers because it provides mathematical formulas and computational flowcharts for computing the derivatives that are needed to implement steepest descent parameter tuning algorithms for such systems it explains why computing such derivatives is much more challenging than it is for a type 1 fuzzy logic system it provides derivative calculations that are applicable to any kind of type 2 membership functions since the calculations are performed without prespecifying the nature of those membership functions some calculations are then illustrated for specific type 2 membership functions

fuzzy adaptive filters with application to nonlinear channel equalization two fuzzy adaptive filters are developed one uses a recursive least squares rls adaptation algorithm and the other uses a least mean square lms adaptation algorithm the rls fuzzy adaptive filter is constructed through the following four steps 1 define fuzzy sets in the filter input space rn whose membership functions cover u 2 construct a set of fuzzy if then rules which either come from human experts or are determined during the adaptation procedure by matching input output data pairs 3 construct a filter based on the set of rules and 4 update the free parameters of the filter using the rls algorithm the design procedure for the lms fuzzy adaptive filter is similar the most important advantage of the fuzzy adaptive filters is that linguistic information in the form of fuzzy if then rules and numerical information in the form of input output pairs can be combined in the filters in a uniform fashion the filters are applied to nonlinear communication channel equalization problems

remarks on space time codes including a new lower bound and an improved code this article presents a new asymptotically exact lower bound on pairwise error probability of a space time code as well as an example code that outperforms the comparable orthogonal design based space time odst code also contained in the article are an exact expression for pairwise error probability pep signal design guidelines and some observations relating to the reception of odst codes

cayley differential unitary space time codes one method for communicating with multiple antennas is to encode the transmitted data differentially using unitary matrices at the transmitter and to decode differentially without knowing the channel coefficients at the receiver since channel knowledge is not required at the receiver differential schemes are ideal for use on wireless links where channel tracking is undesirable or infeasible either because of rapid changes in the channel characteristics or because of limited system resources although this basic principle is well understood it is not known how to generate good performing constellations of unitary matrices for any number of transmit and receive antennas and for any rate this is especially true at high rates where the constellations must be rapidly encoded and decoded we propose a class of cayley codes that works with any number of antennas and has efficient encoding and decoding at any rate the codes are named for their use of the cayley transform which maps the highly nonlinear stiefel manifold of unitary matrices to the linear space of skew hermitian matrices this transformation leads to a simple linear constellation structure in the cayley transform domain and to an information theoretic design criterion based on emulating a cauchy random matrix moreover the resulting cayley codes allow polynomial time near maximum likelihood ml decoding based on either successive nulling canceling or sphere decoding simulations show that the cayley codes allow efficient and effective high rate data transmission in multiantenna communication systems without knowing the channel

generalized receding horizon control of fuzzy systems based on numerical optimization algorithm the optimal control of fuzzy systems with constraints is still an open problem our focus concerns the optimal control problem of fuzzy systems derived from receding horizon control rhc schemes we consider methods to numerically compute the value function for general fuzzy systems the numerical method that is developed using the finite difference with sigmoidal transformation is a stable and convergent algorithm for the hamilton jacobi bellman hjb equation an optimization procedure is developed to increase the calculation accuracy with less computation time a parallel processing method is employed in the optimization procedure the optimization results are applied to the controller design of general fuzzy dynamic systems employing the principle of conventional rhc schemes rhc form controllers are designed for some classes of fuzzy dynamic systems the basic ideas are as follows first the value function is calculated by numerical methods then the value function is used as controller design parameters to redesign rhc controllers for fuzzy systems which is motivated by the inverse lyapunov function design method it is proven that the closed loop system is asymptotically stable an engineering implementation of the controller redesign scheme is discussed meanwhile the parallel processing framework that can improve the closed loop performance is also introduced

on km algorithms for solving type 2 fuzzy set problems computing the centroid and performing type reduction for type 2 fuzzy sets and systems are operations that must be taken into consideration karnik mendel km algorithms are the standard ways to do these operations however because these algorithms are iterative much research has been conducted during the past decade about centroid and type reduction computations this tutorial paper focuses on the research that has been conducted to 1 improve the km algorithms 2 understand the km algorithms leading to further improved algorithms 3 eliminate the need for km algorithms 4 use the km algorithms to solve other nonfuzzy logic system problems and 5 use or not use km algorithms for general type 2 fuzzy sets and fuzzy logic systems

fuzzy adaptive learning control network with on line neural learning abstract this paper addresses the structure and the associated on line learning algorithms of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller the proposed fuzzy adaptive learning control network falcon can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability the connectionist structure of the proposed falcon can be constructed from training examples by neural learning techniques to find proper fuzzy partitions membership functions and fuzzy logic rules two complementary on line structure parameter learning algorithms falcon fsm and falcon art are proposed for constructing the falcon dynamically the falcon fsm combines the backpropagation learning scheme for parameter learning and a fuzzy similarity measure for structure learning the falcon fsn can find proper fuzzy logic rules membership functions and the size of output partitions simultaneously in the falcon fsm algorithm the input and output spaces are partitioned into grids the grid typed space partitioning certainly makes both the fuzzy logic controller software emulation and fuzzy chip implementation convenient however as the number of input output variables increases the number of partitioned grids will grow combinatorially to avoid the problem of combinatorial growth of partitioned grids in some complex systems the falcon art algorithm is developed which can partition the input and output spaces in a more flexible way based on the distribution of the training data the falcon art combines the backpropagation learning scheme for parameter learning and a fuzzy art algorithm for structure learning the falcon art can on line partition the input and output spaces tune membership functions and find proper fuzzy logic rules dynamically computer simulations were conducted to illustrate the performance and applicability of both falcon fsm and falcon art learning algorithms

decomposition property of fuzzy systems and its applications this paper presents the decomposition property of fuzzy systems using a simple constructive decomposition procedure that is by properly dividing the input space into sub input spaces a general fuzzy system is decomposed into several sub fuzzy systems which are the simplest fuzzy systems in the sub input spaces based on the decomposition property of fuzzy systems the analysis of fuzzy systems can be divided into two steps first analyze the properties of the simplest fuzzy systems and then use the decomposition property to extend the results to general fuzzy systems using this idea two applications of the decomposition property are given the first is the application to the representation capability analysis of fuzzy systems the second is the application to the analysis of a class of nonlinear control systems then based on the piecewise affine fuzzy system model the existence condition and the design of a stable control for a class of single input single output siso nonlinear systems are presented

differential space time modulation space time coding and modulation exploit the presence of multiple transmit antennas to improve the performance on multipath radio channels thus far most work on space time coding has assumed that perfect channel estimates are available at the receiver in certain situations however it may be difficult or costly to estimate the channel accurately in which case it is natural to consider the design of modulation techniques that do not require channel estimates at the transmitter or receiver we propose a general approach to differential modulation for multiple transmit antennas based on group codes this approach ran be applied to any number of transmit and receive antennas and any signal constellation we also derive low complexity differential receivers error bounds and modulator design criteria which we use to construct optimal differential modulation schemes for two transmit antennas these schemes can be demodulated with or without channel estimates this permits the receiver to exploit channel estimates when they are available the performance degrades by approximately 3 db when estimates are not available

adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators recently through the use of parameterized fuzzy approximators various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood an important class of parameterized fuzzy approximators is constructed using radial basis function rbf as a membership function however some tuneable parameters in rbf appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task in this paper we propose a new adaptive control method in an effort to tune all the rbf parameters thereby reducing the approximation error and improving control performance global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller simulations performed on a simple nonlinear system illustrate the approach

analyses for l p μ norm approximation capability of generalized mamdani fuzzy systems abstract the connected piecewise linear functions are introduced to study the l p norm approximation capability of the generalized mamdani fuzzy system it is shown that such general fuzzy systems are the universal approximators to the p integrable functions with l p norm for the given degree of accuracy an upper bound of the fuzzy rule numbers needed in the fuzzy rule base of the system is estimated finally a simulation example is analyzed to demonstrate our conclusions

all optimal hankel norm approximations of linear multivariable systems and their l error bounds the problem of approximating a multivariable transfer function g s of mcmillan degree n by s of mcmillan degree k is considered a complete characterization of all approximations that minimize the hankel norm is derived the solution involves a characterization of all rational functions s f s that minimize where s has mcmillan degree k and f s is anticavisal the solution to the latter problem is via results on balanced realizations all pass functions and the inertia of matrices all in terms of the solutions to lyapunov equations it is then shown that where k 1 g s is the k l st hankel singular value of g s and for one class of optimal hankel norm approximations the method is not computationally demanding and is applied to a 12 state model

on monodromies of a degeneration of irreducible symplectic kahler manifolds we study the monodromy operators on the betti cohomologies associated to a good degeneration of irreducible symplectic manifold and we show that the unipotency of the monodromy operator on the middle cohomology is at least the half of the dimension this implies that the mildest singular fiber of a good degeneration with non trivial monodromy of irreducible symplectic manifolds is quite different from the generic degeneration of abelian varieties or calabi yau manifolds

adaptive fuzzy control of uncertain stochastic nonlinear systems with unknown dead zone using small gain approach this paper considers the adaptive fuzzy robust control problem for a class of single input and single output siso stochastic nonlinear systems in strict feedback form the systems under study possess unstructured uncertainties unknown dead zone uncertain dynamics and unknown gain functions in the controller design fuzzy logic systems are adopted to approximate the unknown functions and the uncertain nonlinear system is therefore transformed into an uncertain parameterized system with unmodeled dynamics by combining the backstepping technique with the stochastic small gain approach a novel adaptive fuzzy robust control scheme is developed it is shown that the proposed control approach can guarantee that the closed loop system is input state practically stable isps in probability and the output of the system converges to a small neighborhood of the origin by appropriately tuning several design parameters simulation results are provided to illustrate the effectiveness of the proposed control approach

parameter conditions for monotonic takagi sugeno kang fuzzy system this paper presents sufficient conditions on the parameters of the takagi sugeno kang tsk fuzzy system under which the output of the tsk fuzzy system becomes monotonic with respect to its input the output of the tsk fuzzy system is first differentiated with respect to its input and the parameter conditions are derived so as to make the derivative nonnegative the parameter conditions are developed for both single input and multi input tsk fuzzy systems where the involved fuzzy membership functions are differentiable everywhere or bar some finite points the derived monotonicity conditions consist of two parts the conditions on the consequent parts and the conditions on the fuzzy membership functions they are further characterized for the most important types of fuzzy membership functions

representation theory for high rate multiple antenna code design multiple antennas can greatly increase the data rate and reliability of a wireless communication link in a fading environment but the practical success of using multiple antennas depends crucially on our ability to design high rate space time constellations with low encoding and decoding complexity it has been shown that full transmitter diversity where the constellation is a set of unitary matrices whose differences have nonzero determinant is a desirable property for good performance we use the powerful theory of fixed point free groups and their representations to design high rate constellations with full diversity furthermore we thereby classify all full diversity constellations that form a group for all rates and numbers of transmitter antennas the group structure makes the constellations especially suitable for differential modulation and low complexity decoding algorithms the classification also reveals that the number of different group structures with full diversity is very limited when the number of transmitter antennas is large and odd we therefore also consider extensions of the constellation designs to nongroups we conclude by showing that many of our designed constellations perform excellently on both simulated and real wireless channels

a characteristic point based fuzzy inference system aimed to minimize the number of fuzzy rules this paper presents a characteristic point based fuzzy inference system cpfis for fuzzy modeling from training data the aim of the cpfis is not only satisfactory precision performance but also to employ as few purely linguistic fuzzy rules as possible by using a minimization based systematic training method characteristic points cps are defined as the few data points among the original training data which when they are directly mapped to fuzzy rules and thus form the entire rule base allow the underlying system to be effectively modeled three minimization based algorithms in a sequence are proposed to train the cpfis a gradient projection method a gauss jordan elimination based column elimination and back propagation the cps are determined by iterative computations of the first two minimization algorithms after which the resulting fuzzy sets are further fine tuned by the third algorithm experiments conducted on three benchmark problems showed that the cpfis used one of the smallest number of fuzzy rules among the reported results for other methods the gaussian membership functions in both the input and output fuzzy sets and the small number of fuzzy rules make the rule interpretation of the cpfis much easier than that of other methods thus enhancing human computer cooperation in knowledge discovery

unitary space time modulation for multiple antenna communications in rayleigh flat fading motivated by information theoretic considerations we propose a signaling scheme unitary space time modulation for multiple antenna communication links this modulation is ideally suited for rayleigh fast fading environments since it does not require the receiver to know or learn the propagation coefficients unitary space time modulation uses constellations of t spl times m space time signals spl phi sub i l 1 l where t represents the coherence interval during which the fading is approximately constant and m t is the number of transmitter antennas the columns of each spl phi sub i are orthonormal when the receiver does not know the propagation coefficients which between pairs of transmitter and receiver antennas are modeled as statistically independent this modulation performs very well either when the signal to noise ratio snr is high or when t spl gt m we design some multiple antenna signal constellations and simulate their effectiveness as measured by bit error probability with maximum likelihood decoding we demonstrate that two antennas have a 6 db diversity gain over one antenna at 15 db snr

symmetric products of a semistable degeneration of surfaces we explicitly construct a v normal crossing gorenstein canonical model of the relative symmetric products of a local semistable degeneration of surfaces without a triple point by means of toric geometry using this model we calculate the stringy e polynomial of the relative symmetric product we also construct a minimal model of degeneration of hilbert schemes explicitly

robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors this paper describes the design of the robust adaptive fuzzy controller for uncertain single input single output nonlinear dynamical systems with unknown nonlinearities these unknown nonlinearities are approximated by the fuzzy system with a set of fuzzy if then rules whose parameters are adjusted on line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory the lyapunov synthesis approach is used to develop a state feedback adaptive control algorithm based on the adaptive fuzzy model the key assumption is that the reconstruction errors satisfy certain bounding conditions the control law consists of two terms one is the certainty equivalent control and the other is the bounding control a bounding parameter adaptive law is used to obtain this bounding control the overall control system guarantees that the tracking error converges in the small neighborhood of zero and that all signals involved are uniformly bounded it is also shown that in the special case the tracking error exponentially converges to zero even though the approximation errors exist

multiplication free radial basis function network for the purpose of adaptive function approximation a new radial basis function network is proposed which is nonlinear in its parameters the goal is to reduce significantly the computational effort for a serial processor by avoiding multiplication in both the evaluation of the function model and the computation of the parameter adaptation the approximation scheme makes use of a grid based gaussian basis function network due to the local support of digitally implemented gaussian functions the function representation is parametric local and therefore well suited for an online implementation on a microcomputer a gradient descent based nonlinear learning algorithm is presented and the convergence of the algorithm is proved

stability and the fuzzy controller abstract this paper corrects a part of the main result theorem 1 in buckley fuzzy sets and systems 77 1996 167 173

direct adaptive fuzzy control for nonlinear systems with time varying delays this paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict feedback systems with time varying delays based on the razumikhin function approach a novel adaptive fuzzy controller is designed the proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed loop system remain bounded different from the existing adaptive fuzzy control methodology the fuzzy logic systems are used to model the desired but unknown control signals rather than the unknown nonlinear functions in the systems as a result the proposed adaptive controller has a simpler form and requires fewer adaptation parameters

adaptive control for nonlinear mimo time delay systems based on fuzzy approximation this paper focuses on the problem of adaptive control for a class of nonlinear multi input and multi output mimo systems with time delays a state feedback adaptive controller is constructed by backstepping technique fuzzy logic systems are used to approximate unknown nonlinear functions in the process of the controller design the main advantages of the proposed approach are twofold 1 the controller design is independent of the knowledge of the basis functions of fuzzy logic systems and 2 the suggested approach requires only m adaptive laws to control a nonlinear time delay system with m inputs simulation results illustrate the effectiveness of the proposed approach

on the optimum design of unitary cyclic group space time codes in this letter based on the exact pairwise error probability we derive the union bound on the symbol error probability sep of the differential unitary space time dust modulation employing group codes instead of using the rank and determinant or euclidean distance criteria we optimize the cyclic group codes such that the union bound on the sep is minimized for a predetermined scenario taking into account the number of transmit and receive antennas and the operating signal to noise ratio snr our simulation results show that for a wide range of snrs the codes with the minimum union bound for a particular snr outperform the codes designed based on rank and determinant or euclidean distance criteria

extended neuro fuzzy models of multilayer perceptrons in this paper the famous neural model the multilayer perceptron is extended to a new neural model that is called the additive takagi sugeno type multilayer perceptron the present study proves that this new model can also act as a universal approximator and thus it can be used in many fields such as system modeling and identification the concept of f duality and the fuzzy operator interactive or are used to prove that the proposed neural model is functionally equal to a kind of fuzzy inference system further this paper presents another new neuro fuzzy model that is called the sigmoid adaptive network based fuzzy inference system simulation studies show that our proposed models both have stronger approximation capability than multilayer perceptrons

performance enhancement for t s fuzzy control using neural networks a new control scheme is proposed to improve the system performance for takagi sugeno t s fuzzy system using control grade functions tuned by neural networks first systematic modeling method is introduced to construct the exact t s fuzzy model for a nonlinear control system for the t s fuzzy model the system uncertainty affects only the membership functions to cope with this problem the grade functions resulting from the membership functions of the control rules are tuned by a back propagation network on the other hand the feedback gains of the control rules are determined by solving a set of linear matrix inequalities lmis which satisfy sufficient conditions of the closed loop stability as a result both stability guarantee and better performance are concluded the scheme is applied to a ball and beam system example verified by numerical simulations

interaction measures for control configuration selection based on interval type 2 takagi sugeno fuzzy model interaction measure determines decentralized and sparse control configurations for a multivariable process control this paper investigates interval type 2 takagi sugeno fuzzy it2tsf model based interaction measures using two different criteria one is controllability and observability gramians the other is relative normalized gain array rnga the main contributions are first a data driven it2tsf modeling method is introduced second explicit formulas to execute the two measures based on it2tsf models are given third two interaction indexes are defined from rnga to select sparse control configuration fourth the calculations to derive sensitivities of the two measures with respect to parametric variations in the it2tsf models are developed and fifth the discussion to compare the two measures is presented three multivariable processes are used as examples to show that the results calculated from it2tsf models are more accurate than that from their type 1 counterparts and compared to gramian based measure rnga selects more reasonable control configurations and is more robust to the parametric uncertainties

on direct construction of fuzzy systems abstract in this paper we show that the fuzzy logic system presented by wang and mendel 1992 is a linear expansion of the fuzzy rule base if fuzzy events are represented by triangular basis functions therefore based on linear regression theory we propose a direct approach for constructing a fuzzy logic system from input output data the effectiveness of this approach is demonstrated by simulations

a clustering algorithm for fuzzy model identification the fuzzy model proposed by takagi and sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules in this paper the model is firstly modified to make its identification easier based on the fuzzy c partition space four criteria are proposed for optimization of the model parameters following that a clustering algorithm composed of fuzzy c linear functions clustering and like fuzzy c means clustering is developed for minimizing the four criteria an identification scheme for rule s premise and consequence parameters is deduced from the clustering algorithm in succession finally four examples are demonstrated to verify the effectiveness of the proposed algorithm

support vector learning for fuzzy rule based classification systems to design a fuzzy rule based classification system fuzzy classifier with good generalization ability in a high dimensional feature space has been an active research topic for a long time as a powerful machine learning approach for pattern recognition problems the support vector machine svm is known to have good generalization ability more importantly an svm can work very well on a high or even infinite dimensional feature space this paper investigates the connection between fuzzy classifiers and kernel machines establishes a link between fuzzy rules and kernels and proposes a learning algorithm for fuzzy classifiers we first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function fuzzy inference on the if part of a fuzzy rule can be viewed as evaluating the kernel function the kernel function is then proven to be a mercer kernel if the reference functions meet a certain spectral requirement the corresponding fuzzy classifier is named positive definite fuzzy classifier pdfc a pdfc can be built from the given training samples based on a support vector learning approach with the if part fuzzy rules given by the support vectors since the learning process minimizes an upper bound on the expected risk expected prediction error instead of the empirical risk training error the resulting pdfc usually has good generalization moreover because of the sparsity properties of the svms the number of fuzzy rules is irrelevant to the dimension of input space in this sense we avoid the curse of dimensionality finally pdfcs with different reference functions are constructed using the support vector learning approach the performance of the pdfcs is illustrated by extensive experimental results comparisons with other methods are also provided

quaternion based adaptive output feedback attitude control of spacecraft using chebyshev neural networks this paper investigates the problem of output feedback attitude control of an uncertain spacecraft two robust adaptive output feedback controllers based on chebyshev neural networks cnn termed adaptive neural networks nn controller i and adaptive nn controller ii are proposed for the attitude tracking control of spacecraft the four parameter representations quaternion are employed to describe the spacecraft attitude for global representation without singularities the nonlinear reduced order observer is used to estimate the derivative of the spacecraft output and the cnn is introduced to further improve the control performance through approximating the spacecraft attitude motion the implementation of the basis functions of the cnn used in the proposed controllers depends only on the desired signals and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the cnn approximation errors and external disturbances the adaptive nn controller ii can efficiently avoid the over estimation problem i e the bound of the cnns output is much larger than that of the approximated unknown function and hence the control input may be very large existing in the adaptive nn controller i both adaptive output feedback controllers using cnn can guarantee that all signals in the resulting closed loop system are uniformly ultimately bounded for performance comparisons the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed simulation studies are presented to show the advantages of the proposed cnn based output feedback approach over the standard adaptive output feedback approach

adaptive fuzzy sliding mode control of nonlinear system in this paper the fuzzy approximator and sliding mode control smc scheme are considered we propose two methods of adaptive smc schemes that the fuzzy logic systems approximators are used to approximate the unknown system functions in designing the smc of nonlinear system in the first method a fuzzy logic system is utilized to approximate the unknown function f of the nonlinear system x sup n f x t b x t u and the robust adaptive law is proposed to reduce the approximation errors between the true nonlinear functions and fuzzy approximators in the second method two fuzzy logic systems are utilized to approximate the f and b respectively and the control law which is robust to approximation error is also designed the stabilities of proposed control schemes are proved and these schemes are applied to an inverted pendulum system the comparisons between the proposed control schemes are shown in simulations

second order sliding fuzzy interval type 2 control for an uncertain system with real application a new second order sliding mode type 2 fuzzy controller for nonlinear uncertain perturbed systems is developed in this paper to overcome the constraint on the knowledge of the system model we have used local models that are related to some operating points to synthesize a type 2 nominal fuzzy global model the control is based on the super twisting algorithm which is among second order sliding mode controls smcs moreover two adaptive fuzzy type 2 systems have been introduced to generate the two super twisting signals to avoid both the chattering and the constraint on the knowledge of disturbances and uncertainties upper bounds these adaptive fuzzy type 2 systems are adjusted online by two adaptation laws that are deduced from the stability analysis in the lyapunov sense it has only one input i e the sliding surface and one output i e the optimal values of the gains control which are hard to compute with the original algorithm many results of the one link manipulator are obtained first by the simulation in order to compare the performances of the proposed method with that given by levant and then in a real time application in order to confirm the efficiency of the proposed approach the experimentation and simulation are done for the tracking control problem

term and variable selection for non linear system identification the purpose of variable selection is to pre select a subset consisting of the significant variables or to eliminate the redundant variables from all the candidate variables of a system under study prior to model term detection it is required that the selected significant variables alone should sufficiently represent the system generally not all the model terms which are produced by combining different variables make an equal contribution to the system output and terms which make little contribution can be omitted a parsimonious representation which contains only the significant terms can often be obtained without the loss of representational accuracy by eliminating the redundant terms based on these observations a new variable and term selection algorithm is proposed in this paper the term detection algorithm can be applied to the general class of non linear modelling problems which can be expressed as a linear in the parameters form the variable selection procedure is based on locally linea

recursive least squares identification algorithms with incomplete excitation convergence analysis and application to adaptive control the convergence properties of a fairly general class of adaptive recursive least squares algorithms are studied under the assumption that the data generation mechanism is deterministic and time invariant first the open loop identification case is considered by a suitable notion of excitation subspace the convergence analysis of the identification algorithm is carried out with no persistent excitation hypothesis i e it is proven that the projection of the parameter error on the excitation subspace tends to zero while the orthogonal component of the error remains bounded the convergence of an adaptive control scheme based on the minimum variance control law is then dealt with it is shown that under the standard minimum phase assumption the tracking error converges to zero whenever the reference signal is bounded furthermore the control variable turns out to be bounded

observer based adaptive fuzzy control for siso nonlinear systems abstract the observer based indirect and direct adaptive fuzzy controllers are developed for a class of siso uncertain nonlinear systems the proposed approaches do not need the availability of the state variables by designing the state observer the adaptive fuzzy systems which are used to model the unknown functions can be constructed using the state estimations thus a new hybrid adaptive fuzzy control method is proposed by combining the above adaptive fuzzy system with the h control technique based on lyapunov stability theorem the proposed adaptive fuzzy control system can guarantee the stability of the whole closed loop systems and obtain good tracking performance as well the proposed methods are applied to an inverted pendulum system and a chaotic system and achieve satisfactory simulation results

orthogonal least squares methods and their application to non linear system identification abstract identification algorithms based on the well known linear least squares methods of gaussian elimination cholesky decomposition classical gram schmidt modified gram schmidt householder transformation givens method and singular value decomposition are reviewed the classical gram schmidt modified gram schmidt and householder transformation algorithms are then extended to combine structure determination or which terms to include in the model and parameter estimation in a very simple and efficient manner for a class of multivariate discrete time non linear stochastic systems which are linear in the parameters

design of fuzzy wavelet neural networks using the ga approach for function approximation and system identification in this paper an efficient method is proposed to design fuzzy wavelet neural network fwnn for function learning and identification by tuning fuzzy membership functions and wavelet neural networks the structure of fwnn is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules in order to improve the function approximation accuracy and general capability of the fwnn system an efficient genetic algorithm ga approach is used to adjust the parameters of dilation translation weights and membership functions by minimizing a quadratic measure of the error derived from the output of the system the design problem can be characterized by the proposed ga formulation moreover the solution is directly obtained without any need for complicated computations the performance of our approximation is superior to that of existing methods several numerical design examples are likewise presented to demonstrate the design flexibility and usefulness of this presented approach

space time autocoding prior treatments of space time communications in rayleigh flat fading generally assume that channel coding covers either one fading interval in which case there is a nonzero outage capacity or multiple fading intervals in which case there is a nonzero shannon capacity however we establish conditions under which channel codes span only one fading interval and yet are arbitrarily reliable in short space time signals are their own channel codes we call this phenomenon space time autocoding and the accompanying capacity the space time autocapacity let an m transmitter antenna n receiver antenna rayleigh flat fading channel be characterized by an m spl times n matrix of independent propagation coefficients distributed as zero mean unit variance complex gaussian random variables this propagation matrix is unknown to the transmitter it remains constant during a t symbol coherence interval and there is a fixed total transmit power let the coherence interval and number of transmitter antennas be related as t spl beta m for some constant spl beta a t spl times m matrix valued signal associated with r spl middot t bits of information for some rate r is transmitted during the t symbol coherence interval then there is a positive space time autocapacity c sub a such that for all r c sub a the block probability of error goes to zero as the pair t m spl rarr spl infin such that t m spl beta the autocoding effect occurs whether or not the propagation matrix is known to the receiver and c sub a nlog 1 spl rho in either case independently of spl beta where spl rho is the expected signal to noise ratio snr at each receiver antenna lower bounds on the cutoff rate derived from random unitary space time signals suggest that the autocoding effect manifests itself for relatively small values of t and m for example within a single coherence interval of duration t 16 for m 7 transmitter antennas and n 4 receiver antennas and an 18 db expected snr a total of 80 bits corresponding to rate r 5 can theoretically be transmitted with a block probability of error less than 10 sup 9 all without any training or knowledge of the propagation matrix

nonlinear control via approximate input output linearization the ball and beam example approximate input output linearization of nonlinear systems which fail to have a well defined relative degree is studied for such systems a method for constructing approximate systems that are input output linearizable is provided the analysis presented is motivated through its application to a common undergraduate control laboratory experiment the ball and beam where it is shown to be more effective for trajectory tracking than the standard jacobian linearization

adaptive control of a class of nonlinear pure feedback systems using fuzzy backstepping approach a controller is proposed for the robust backstepping control of a class of nonlinear pure feedback systems using fuzzy logic the proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the usual controllers and the approximation errors can be efficiently counteracted by employing smooth robust compensators most importantly the uniform ultimate boundedness of all signals in the closed loop system can be guaranteed and a priori knowledge of the plant dynamics is no longer required furthermore the proposed method can be used for adaptive control of a large class of single input single output nonlinear systems in both strict feedback and pure feedback forms and has great potential in many diverse applications the performance of the proposed approach is demonstrated through three simulation examples including one nonlinear pure feedback and two nonlinear strict feedback systems

performance of space time codes for a large number of antennas we study the asymptotic behavior of space time codes when the number of transmit and receive antennas grows to infinity specifically we determine the behavior of pairwise error probabilities with maximum likelihood ml decoding and with three types of receiver interfaces the ml interface the linear zero forcing zf interface and the linear minimum mean square error mmse interface two situations are studied when the number of receiving antennas grows to infinity while the number of transmitting antennas is finite and when both numbers grow to infinity but their ratio remains constant we show that with ml or linear interfaces the asymptotic performance of space time codes is determined by the euclidean distances between codewords moreover with the two linear interfaces examined here the number r of receive antennas must be much larger than the number t of transmit antennas to avoid a sizeable loss of performance on the other hand when r spl gt t the performance of these linear interfaces comes close to that of ml the dependence of error probabilities on euclidean distance is valid for intermediate signal to noise ratios snrs even when the number of antennas is small simulations validate our theoretical findings and show how asymptotic results may be substantially valid even in a nonasymptotic regime thus even for few antennas off the shelf codes may outperform space time codes designed ad hoc

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

improved structure optimization for fuzzy neural networks fuzzy neural network based inference systems are well known universal approximators which can produce linguistically interpretable results unfortunately their dimensionality can be extremely high due to an excessive number of inputs and rules which raises the need for overall structure optimization in the literature various input selection methods are available but they are applied separately from rule selection often without considering the fuzzy structure this paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously first a method is developed to select the most significant rules along with a refinement stage to remove unnecessary correlations an improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model leading to a balanced tradeoff between interpretability and accuracy simulation results confirm the efficacy of the proposed method

direct robust adaptive fuzzy control drafc for uncertain nonlinear systems using small gain theorem abstract a novel direct robust adaptive fuzzy control drafc is proposed for a class of nonlinear systems with uncertain system and gain functions which both are unstructured or non repeatable state dependent unknown nonlinear functions takagi sugeno type fuzzy logic systems are used to approximate the uncertain system function and the drafc algorithm is designed by use of input to state stability iss approach and small gain theorem the resulting closed loop system is proven to be semi globally uniformly ultimately bounded in addition the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with only one learning parameterization can be achieved the control performance of the closed loop system is guaranteed by appropriately choosing the design parameters an example illustrating the proposed method is included for a ship autopilot system to maintain the ship on a pre determined heading simulation results show the effectiveness of the control scheme

necessary conditions for some typical fuzzy systems as universal approximators abstract we investigate necessary conditions for general single input single output fuzzy systems and a class of typical multiple input single output fuzzy systems as universal approximators for continuous functions defined on compact domains with arbitrarily small uniform approximation error bounds considering the case where the only available data about the continuous function to be approximated are a finite set of its extrema we have established some necessary conditions for the fuzzy systems to be universal approximators of the function the conditions can be used practically to determine input fuzzy sets output fuzzy sets and fuzzy rules of the fuzzy systems furthermore these necessary conditions provide a basis for insightful analysis of the strengths as well as the limitations of the fuzzy systems the main strength is that only a small number of fuzzy rules may be needed to uniformly approximate continuous functions that have a complicated formulation but a relatively small number of extrema the limitation is that in order to approximate highly oscillatory continuous functions the number of fuzzy rules must be large 1997 elsevier science ltd

technical communique fixed modes in multivariable systems using constrained controllers a general expression is derived relating an arbitrary output feedback controller to the resulting closed loop characteristic polynomial in terms of suitably defined zero polynomials of the system this expression is then used to characterize the fixed modes of a multivariable system with respect to controllers of any desired structure

orthogonal least squares learning algorithm for radial basis function networks the radial basis function network offers a viable alternative to the two layer neural network in many applications of signal processing a common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network such a procedure has several drawbacks and in particular an arbitrary selection of centers is clearly unsatisfactory the authors propose an alternative learning procedure based on the orthogonal least squares method the procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed in the algorithm each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill conditioning problems the orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks this is illustrated using examples taken from two different signal processing applications

motivic integration over deligne mumford stacks abstract the aim of this article is to develop the theory of motivic integration over deligne mumford stacks and to apply it to the birational geometry of deligne mumford stacks

advanced fuzzy inference engines in situation aware computing we focus on the very important family of context aware applications context aware computing relies on tasks like capturing sensing environmental parameters e g lightness location classifying context and inferring further knowledge about that context determine the situation the user is currently in however the relevant applications have to deal with the inherent imperfection of context sensing for decision making we propose an extension of context representation and inference for situation aware applications our model relies on fuzzy set theory to accommodate the imperfection of sensed context based on this model we have developed three fuzzy inference engines which rely on advanced semantics specialization mereological and compatibility relations we have evaluated the proposed engines through a series of experiments involving real users our findings indicate the strong points of the proposed context classification and inference processes

gain tuning of fuzzy pid controllers for mimo systems a performance driven approach a new methodology for tuning the scaling factors or gains of fuzzy proportional integral derivative controllers by taking explicitly into account the closed loop system performance is proposed in this study the solution is obtained by solving a nonlinear constrained optimization problem considering a set of constraints on the scaling factors of the mamdani type fuzzy system and on the plant s inputs and outputs two distinct approaches are presented which are associated with the optimization being carried out offline or in real time the offline tuning scheme assumes the system dynamics described by a nonlinear model while for the real time implementation the plant s dynamics is locally approximated by a linear model with the underlying parameters recursively updated in order to cope with rather stringent sampling time requirements the constrained online optimization problem is implemented based on the grid computing paradigm given the adaptive nature of the real time scheme time varying dynamics and unknown disturbances can be accommodated in such a way that the closed loop performance is effectively maximized while avoiding wind up phenomena induced by the integrator term the proposed tuning methodologies are assessed on a benchmark three tank system and compared against a conventional based tuning approach results from experiments illustrate the feasibility of the proposed approaches and also all the relevance in optimal control systems based on mamdani type fuzzy controllers

robust adaptive fuzzy control in the presence of external disturbance and approximation error robust direct and indirect adaptive fuzzy controllers are proposed for tracking control of a class of nonlinear siso systems the behavior of the adaptive law is investigated in the error state space when the perturbations are introduced in the adaptive fuzzy control system a new adaptation scheme that can eliminate the effect of perturbations on the parameter adaptation is proposed it is proved that all the signals in the closed loop system are bounded in the presence of external disturbances and approximation error of the fuzzy logic system the concept of persistent excitation is utilized to guarantee the convergence and the boundedness of adaptation parameters it has also been observed that the convergence of parameters and tracking error could be smoother and faster with the proposed methods compared with the conventional adaptive fuzzy controllers two simulations have been conducted to demonstrate the effectiveness of the proposed controllers

unitary signal constellations for differential space time modulation with two transmit antennas parametric codes optimal designs and bounds we focus on the design of unitary signal constellations for differential space time modulation with double transmit antennas by using the parametric form of a two by two unitary matrix we present a class of unitary space time codes called parametric codes and show that this class of unitary space time codes leads to a five signal constellation with the largest possible diversity product and a 16 signal constellation with the largest known diversity product although the parametric code of size 16 is not a group by itself we show that it is a subset of a group of order 32 furthermore the unitary signal constellations of sizes 32 64 128 and 256 obtained by taking the subsets of the parametric codes of sizes 37 75 135 and 273 respectively have the largest known diversity products we also use large diversity sum of unitary space time signal constellations as another significant property for the signal constellations to have good performance in low snr scenarios the newly introduced unitary space time codes can lead to signal constellations with sizes of 5 and 9 through 16 that have the largest possible diversity sums subsequently we construct a few sporadic unitary signal constellations with the largest possible diversity product or diversity sum a four signal constellation which has both the largest possible diversity product and the largest possible diversity sum and three unitary signal constellations with the largest possible diversity sums for sizes of 6 7 and 8 are constructed respectively furthermore by making use of the existing results in sphere packing and spherical codes we provide several upper and lower bounds on the largest possible diversity product and the largest possible diversity sum that unitary signal constellations of any size can achieve

a note on smooth approximation capabilities of fuzzy systems modeling and prediction in some systems requires the simultaneous approximation of mappings and their derivatives to a certain finite order in this paper universal approximation capabilities of fuzzy systems are extended to this situation by showing the denseness of some general classes of fuzzy models in appropriate function spaces where distance between functions is defined in terms of their derivatives requirements are generally very mild and are often limited to imposing sufficient differentiability conditions on the classes of fuzzy systems to be used the cases covered by this paper include additive fuzzy systems with gaussian membership functions and general additive fuzzy models with realistic membership functions some potential application fields are considered including examples from economics and time series prediction

differential unitary space time modulation we present a framework for differential modulation with multiple antennas across a continuously fading channel where neither the transmitter nor the receiver knows the fading coefficients the framework can be seen as a natural extension of standard differential phase shift keying commonly used in single antenna unknown channel systems we show how our differential framework links the unknown channel system with a known channel system and we develop performance design criteria as a special ease we introduce a class of diagonal signals where only one antenna is active at any time and demonstrate how these signals may be used to achieve full transmitter diversity and low probability of error

comments on fuzzy systems approximation by frames siso case we show that the approach taken by shmilovici and maimon to construct fuzzy wavelet is wrong and the example given is also incorrect

antenna selection for unitary space time modulation this correspondence studies receive antenna selection as for multiple antenna systems that employ unitary space time st signals where the channel state information csi is known neither at the transmitter nor at the receiver without csi at the receiver we perform as only at the receiver and the selection is based on a maximum norm criterion i e a subset of receive antennas that have the largest received signal power is chosen using a chernoff bound approach we present theoretical performance analysis based on the pairwise error probability pep and quantify the asymptotic performance at high signal to noise ratio snr by giving the diversity and coding gain expressions we prove that with no csi at the receiver the diversity gain with as is preserved for unitary st codes with full spatial diversity the same as the case with known csi as a concrete example for differential unitary st modulation with m 2 transmit antennas and n 2 receive antennas we have devised new excellent performing parametric codes based on the derived pep bound the new codes which are specifically designed for differential as systems outperform known differential codes when as is employed corroborating simulations validate our analysis and code design

fuzzy approximation based adaptive control of strict feedback nonlinear systems with time delays this paper focuses on the problem of adaptive control for a class of nonlinear time delay systems with unknown nonlinearities and strict feedback structure based on the lyapunov krasovskii functional approach a state feedback adaptive controller is constructed by backstepping the proposed adaptive controller guarantees that the system output converges into a small neighborhood of the reference signal and all the signals of the closed loop system remain bounded compared with the results that exist the main advantage of the proposed method is that the controller design is independent of the choice of the fuzzy membership functions therefore a priori knowledge of fuzzy approximators is not necessary for control design and the proposed approach requires only one adaptive law for an nth order system two numerical examples are used to illustrate the effectiveness of the proposed approach

control configuration selection for bilinear systems via generalised hankel interaction index array decentralised and partially decentralised control strategies are very popular in practice to come up with a suitable decentralised or partially decentralised control structure it is important to select the appropriate input and output pairs for control design this procedure is called control configuration selection it is well known that a suitable control configuration selection is an important prerequisite for a successful industrial control in this paper the problem of control configuration selection for multiple input and multiple output mimo bilinear processes is addressed first the concept of the cross gramian is developed for bilinear systems the conditions for the existence of generalised cross gramian are derived it is shown that if the cross gramian exists it is the solution to the generalised sylvester equation to obtain the cross gramian in a more computationally efficient way an iterative method for solving the generalised sylvester equation is proposed the generalised cross gram

a fuzzy variable structure approach to feedback regulation of uncertain dynamical systems with application to robotics this paper develops a fuzzy logic based control design methodology for a large class of nonlinear uncertain dynamical systems the proposed control design approach combines the powerful tools of fuzzy logic and approximate reasoning with the advanced mathematical synthesis techniques used in variable structure control systems theory the rationale for the proposed control design approach is motivated by the results of a recent study that has rigorously established some connections between fuzzy logic and variable structure control systems the results of this paper represent a step in the right direction for systematic design of a less expert dependent fuzzy logic control to illustrate the merits of the new control approach the latter is applied to a 2 degree of freedom robot manipulator preliminary simulation results suggest that the proposed control design deals effectively with the chattering problem encountered when the classical variable structure control is used alone

a computed torque controller for uncertain robotic manipulator systems fuzzy approach computed torque control ctc is an effective motion control strategy for robotic manipulator systems which can ensure globally asymptotic stability however ctc scheme requires precise dynamical models of robotic manipulators to handle this impossibility in this paper a new approach combing ctc and fuzzy control fc is developed for trajectory tracking problems of robotic manipulators with structured uncertainty and or unstructured uncertainty fuzzy part with a set of tunable parameters is employed to approximate lumped uncertainty due to parameters variations unmodeled dynamics and so on in robotic manipulators based on lyapunov stability theorem it is shown that the proposed controller can guarantee stability of closed loop systems and satisfactory tracking performances the proposed approach indicates that ctc method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed finally computer simulation results on a two link elbow planar robotic manipulator are presented to show tracking capability and effectiveness of the proposed scheme

approximation capability of fuzzy systems using translations and dilations of one fixed function as membership functions this paper reports on a related study on approximation theory of fuzzy systems first some basic principles are presented to construct membership functions then an approach is proposed to form membership functions by using translations and dilations of one fixed function called a basis function which is very similar to that in wavelets analysis the properties of this type of membership function reflect the advantages of the given approach finally it is proved that fuzzy systems based on such membership functions are universal approximators under certain mild conditions on the basis function this conclusion expands the family of fuzzy systems which can be universal approximators

adaptive control of robot manipulators using fuzzy logic systems under actuator constraints in this paper a stable fuzzy adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements taking into account the actuator constraints the controller is based on structural knowledge of the dynamics of the robot and measurements of link positions only the gravity torque including system uncertainty like payload variation etc is estimated by a fuzzy logic system fls the adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using fls to estimate the gravity torque the proposed controller ensures the local asymptotic stability and the convergence of the position error to zero the proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances the validity of the control scheme is shown by simulations on a two link robot manipulator

variable selection algorithm for the construction of mimo operating point dependent neurofuzzy networks an input variable selection procedure is introduced for the identification and construction of multi input multi output mimo neurofuzzy operating point dependent models the algorithm is an extension of a forward modified gram schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting the proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice based modeling algorithms such as radial basis function neurofuzzy networks enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm

fuzzy adaptive control of nonlinear uncertain plants with unknown dead zone output this paper focuses on a tracking problem for a class of strict feedback nonlinear systems with unknown dead zone output and unmeasurable states based on the adaptive backstepping technique a new adaptive fuzzy control scheme is proposed it is shown that the proposed control scheme guarantees that all the signals in the closed loop system are semi globally uniformly ultimately bounded the tracking error converges to a small neighborhood of the origin moreover the proposed control method requires only one adaptive law for an nth order system and the structure of the controllers is very simple so they are easy to be implemented the main advantage of this paper is that the unknown dead zone nonlinearity in the output mechanism of strict feedback systems is considered by introducing a nussbaum function the problem of unknown virtual control coefficient is resolved which is caused by the nonlinearity in the output mechanism finally simulation results are provided to show the effectiveness of the proposed approach

joint block structure sparse representation for multi input multi output mimo t s fuzzy system identification takagi sugeno t s fuzzy system identification provides a reasonable framework for modeling and approximation by decomposition of a nonlinear system into a collection of local linear models in this paper we extend the multidimensional output fuzzy rule from the corresponding single output fuzzy rule with the important difference in the variable definition of output namely the output of each fuzzy rule is represented by a multidimensional vector instead of a scalar value in this case the consequent of the fuzzy rule with the multidimensional output variable share a common antecedent part different from the traditional methods that separate the multi input multi output fuzzy system into a group of multi input single output fuzzy models in this paper we take into account the block structure information in the t s fuzzy system and cast the problem of multidimensional output fuzzy model identification as a joint structure sparse optimization problem where the consequent parameters are estimated with a common block structured sparsity pattern over all dimensions of the output variable furthermore we exploit a joint block sparse orthogonal match pursuit algorithm to reduce the number of fuzzy rules in terms of all dimensions of the output variable and prove the sufficient conditions in consideration of the multidimensional output together with the block structure in the t s fuzzy model this method is efficient and shows good performance in well known benchmark datasets and real world problems