korovkin tests approximation and ergodic theory we consider sequences of s k n x t k n matrices a n f with a block structure spectrally distributed as an l 1 p variate s x t matrix valued function f and for any n we suppose that a n is a linear and positive operator for every fixed n we approximate the matrix a n f in a suitable linear space m n of s k n x t k n matrices by minimizing the frobenius norm of a n f x n when x n ranges over m n the minimizer x n is denoted by p k n a n f we show that only a simple korovkin test over a finite number of polynomial test functions has to be performed in order to prove the following general facts 1 the sequence p k n a n f is distributed as f 2 the sequence a n f p k n a n f is distributed as the constant function 0 i e is spectrally clustered at zero the first result is an ergodic one which can be used for solving numerical approximation theory problems the second has a natural interpretation in the theory of the preconditioning associated to cg like algorithms

generalization of stability criterion for takagi sugeno continuous fuzzy model the stability analysis of a generalized class of continuous fuzzy systems in terms of lyapunov stability theory is presented firstly the stability problem of fuzzy systems described by takagi sugeno s continuous model is stated secondly new stability conditions which guarantee the stability of the fuzzy system are derived the new stability conditions can be regarded as a general solution for takagi sugeno fuzzy system in which the offset term is not equal to zero finally the suggested stability theorems are verified by some illustrative examples

brief paper fault tolerant control for singular systems with actuator saturation and nonlinear perturbation in this paper the problem of robust fault tolerant control for a class of singular systems subject to both time varying state dependent nonlinear perturbation and actuator saturation is investigated a sufficient condition for the existence of a fixed gain controller is first proposed which guarantees the regularity impulse free and stability of the closed loop system under all possible faults an optimization problem with lmi constraints is formulated to determine the largest contractively invariant ellipsoid an adaptive fault tolerant controller is then developed to compensate for the failure effects on the system by estimating the fault and updating the design parameter matrices online both of these two controllers are in the form of a saturation avoidance feedback with the advantage of relatively small actuator capacities compared with the high gain counterpart an example is included to illustrate the proposed procedures and their effectiveness

robust stabilization of the takagi sugeno fuzzy model via bilinear matrix inequalities quadratic stability has enabled mainly via the linear matrix inequality framework the analysis and design of a nonlinear control system from the local matrices of the system s takagi sugeno t s fuzzy model it is well known however that there exist stable differential inclusions hence t s fuzzy models whose stability is unprovable by a globally quadratic lyapunov function at present literature in the broader area of stability analysis suggests piecewise quadratic stability as a means to avoid such conservatism this paper generalizes the idea and proposes a framework that supports less conservative sufficient conditions for the stability of the t s model by using piecewise quadratic generalized lyapunov functions the advocated approach results in the formulation of the controller synthesis which herein aims for robust stabilization as a problem of bilinear rather than linear matrix inequalities simulation studies which include an algorithm for solution of bilinear matrix inequalities demonstrate the proposed method

successive identification of a fuzzy model and its applications to prediction of a complex system abstract a successive identification method of a fuzzy model is suggested the identification mechanism consists of two levels one is the supervisor level and the other is the adjustment level the supervisor level determines a policy of parameter adjustment using a set of fuzzy adjustment rules the adjustment rules are derived from the fuzzy implications of a fuzzy model and are extended to fuzzy adjustment rules by using an extended concept of zadeh s contrast intensification the adjustment level executes the policy of parameter adjustment determined with the fuzzy adjustment rules the parameter adjustment consists of premise parameter adjustment and consequent parameter adjustment both of them are realized by the weighted recursive least square algorithm finally it is shown from two examples that the method is very useful for modeling complex systems

spectral and computational properties of band symmetric toeplitz matrices abstract we are investigating spectral properties of band symmetric toeplitz matrices bst matrices by giving a suitable representation of a bst matrix we achieve separation results and multiplicity conditions for the eigenvalues of a 7 r 5 diagonal bst matrix and also structural properties of the eigenvectors we give eigenvalue bounds for a 2k 1 diagonal bst matrix and also necessary and sufficient conditions for positive definiteness which are easy to check the same conditions apply in the case of block bst matrices either with full blocks or with bst blocks we exhibit fast computational methods for the evaluation of the determinant and the characteristic polynomial of a bst matrix either for sequential or for parallel computations two algorithms based on the bisection technique and newton s method are shown to be very fast for computing the eigenvalues of a 7 or 5 diagonal bst matrix

observer based robust h sub spl infin control for fuzzy systems using two step procedure this paper investigates both robust h sub spl infin analysis and synthesis problems involving observer based fuzzy control via linear matrix inequality methods for which efficient optimization techniques are available the observer and controller are capable of disturbance rejection in the presence of unknown but bounded disturbance we present results in a unified fashion applicable to both continuous and discrete time problems with or without uncertainty finally the validity and applicability of the approach are demonstrated by examples

a fuzzy logic based approach to qualitative modeling this paper discusses a general approach to quali tative modeling based on fuzzy logic the method of qualitative modeling is divided into two parts fuzzy modeling and linguistic approximation it proposes to use a fuzzy clustering method fuzzy c means method to identify the structure of a fuzzy model to clarify the advantages of the proposed method it also shows some examples of modeling among them a model of a dynamical process and a model of a human operator s control action

on the extreme eigenvalues of hermitian block toeplitz matrices abstract we are concerned with the behavior of the minimum maximum eigenvalue 0 n n n of an n 1 n 1 hermitian toeplitz matrix tn where is an integrable real valued function kac murdoch and szego widom parter and r h chan obtained that 0 n min o 1 n2k in the case where c2k at least locally and inf has a zero of order 2k we obtain the same result under the second hypothesis alone moreover we develop a new tool in order to estimate the extreme eigenvalues of the mentioned matrices proving that the rate of convergence of 0 n to inf depends only on the order not necessarily even or integer or finite of the zero of inf with the help of this tool we derive an absolute lower bound for the minimal eigenvalues of toeplitz matrices generated by nonnegative l1 functions and also an upper bound for the associated euclidean condition numbers finally these results are extended to the case of hermitian block toeplitz matrices with toeplitz blocks generated by a bivariate integrable function

robust h control of multiple time delay uncertain nonlinear system using fuzzy model and adaptive neural network in this paper a robust control method which combines fuzzy model based control with adaptive neural network control is presented for a class of uncertain nonlinear system with multiple time delay the fuzzy t s model with multiple time delay is adopted for the approximate modeling of the nonlinear system with some unknown uncertainties and fuzzy model based h control law is designed by means of lmi method a full adaptive rbf neural network is added to the fuzzy h control in order to guarantee the robust stability of the controlled system the effect of the unknown uncertainties and the error caused by fuzzy modeling can be overcome by adaptive tuning of the weights centers and widths of the rbf neural network on line and no constraint or matching conditions are required the stability of the designed closed loop system is thus proved the proposed method is applied to a multiple time delay nonlinear chaotic system and the simulation results show that the proposed method cannot only stabilize the chaos systems but has strong robustness against uncertainties and external disturbance

analysis and design of fuzzy control systems using dynamic fuzzy global models a method for analysis and design of a fuzzy controller is developed for a class of fuzzy discrete time systems the method is based on a dynamical fuzzy state space model it is shown that the closed loop fuzzy system can be represented by a dynamical interval system and thus the methods of stability analysis in uncertain linear system theory can be used to analyse the global stability of the closed loop fuzzy system some examples are given to illustrate the application of the method

stable controller design for t s fuzzy systems based on lie algebras in this paper we study the stability of fuzzy control systems of takagi sugeno t s type based on the classical theory of lie algebras t s fuzzy systems are used to model nonlinear systems as a set of rules with consequents of the type email protected t a lx t b lu t we conduct the stability analysis of such t s fuzzy models using the lie algebra l a generated by the a l matrices of these subsystems for each rule in the rule base we first develop our approach of stability analysis for a commuting algebra l a where all the consequent state matrices a l s commute we then generalize our results to the noncommuting case the basic idea here is to approximate the noncommuting lie algebra with a commuting one such that the approximation error is minimum the results of this approximation are extended to the most general case using the levi decomposition of lie algebras the theory is applied to the control of a flexible joint robot arm where we also present the decomposition procedure

c g preconditioning for toeplitz matrices abstract we consider the problem of solving a toeplitz system of equations by conjugate gradient method when a sequence of nested toeplitz matrices is associated to a function the spectral behaviour of the matrices involved is closely related to the analytical properties of the generating function thus it is possible to devise efficient preconditioning techniques by using various functional approximation strategies this approach leads to attractive results in the case of ill conditioned matrices for which a wide class of preconditioners are proposed

existence of similarity transformation converting bmis to lmis under a mild condition we investigate the existence of a similarity transformation regarding robust hinfin stability analysis and observer controller synthesis problems the observer based controller is capable of disturbance rejection in the presence of unknown but bounded disturbance we present results which illustrate plainly the role of the similarity transformation in converting a bmi problem into an lmi problem in a unified fashion applicable to both continuous and discrete time problems with or without uncertainty finally the validity and applicability of the approach are illuminated by examples

brief paper robust finite frequency h filtering for uncertain 2 d roesser systems this paper investigates the problem of robust finite frequency ff h filtering for two dimensional 2 d roesser models with polytopic uncertainties our attention is focused on designing filters guaranteeing the robustly asymptotic stability and ff h disturbance attenuation level of the filtering error system by the parameter dependent idea and the generalized kalman yakubovich popov lemma for 2 d roesser models the existence conditions of robust ff h filters are obtained in terms of solving an optimization problem which is more general than the existing results an example is given to validate the proposed methods the contribution of the paper is twofold 1 systematic methods are proposed for designing ff h filters for roesser models 2 an improved strategy has been presented to deal with the robust h filter design for roesser models

semiglobal stabilization of nonlinear systems using fuzzy control and singular perturbation methods singular perturbation methods based on a lyapunov approach are implemented for the derivation of sufficient conditions for the semiglobal stabilization with output tracking of nonlinear systems having internal dynamics the key idea of this approach lies in the decomposition of the full system into a subsystem exhibiting fast dynamics and to a subsystem with slow dynamics resulting in a substantial simplification of the stability analysis this two time scale behavior is achieved by implementing a properly designed fuzzy logic controller the key element of the proposed model based composite controller for the construction of the model based controller a new identification technique based on both classical and fuzzy tools is proposed results are presented for the semiglobal stabilization of a continuous stirred tank reactor which is a process with high nonlinear behavior

multigrid methods for symmetric positive definite block toeplitz matrices with nonnegative generating functions in this paper we introduce a generalized multigrid method for solving linear systems bf t n m bf x bf b where bf t n m in re nm times nm is a symmetric block toeplitz matrix with symmetric toeplitz blocks we use a special choice of the projection operator whose coefficients simply depend on the generating functions associated with the proposed class of matrices this choice leads to iterative methods with convergence rates independent of the euclidean condition number kappa 2 bf t n m and of the dimension of the involved matrices the total arithmetic cost is o nm log nm for dense matrices and o nm for band matrices with band blocks in the pram model only o log nm parallel steps are required this algorithm is therefore competitive with the preconditioned conjugate gradient methods proposed by chan and jin siam j sci statist comput 13 1992 pp 1218 1235 ku and kuo siam j sci statist comput 13 1992 pp 948 966 di benedetto siam j sci comput 17 1996 and serra bit 34 1994 pp 579 594 for dense matrices and improves those results for band block toeplitz matrices with band toeplitz blocks

filtering and fault detection for nonlinear systems with polynomial approximation this paper is concerned with polynomial filtering and fault detection problems for a class of nonlinear systems subject to additive noises and faults the nonlinear functions are approximated with polynomials of a chosen degree different from the traditional methods the approximation errors are not discarded but formulated as low order polynomial terms with norm bounded coefficients the aim of the filtering problem is to design a least squares filter for the formulated nonlinear system with uncertain polynomials and an upper bound of the filtering error covariance is found and subsequently minimized at each time step the desired filter gain is obtained by recursively solving a set of riccati like matrix equations and the filter design algorithm is therefore applicable for online computation based on the established filter design scheme the fault detection problem is further investigated where the main focus is on the determination of the threshold on the residual due to the nonlinear and time varying nature of the system under consideration a novel threshold is determined that accounts for the noise intensity and the approximation errors and sufficient conditions are established to guarantee the fault detectability for the proposed fault detection scheme comparative simulations are exploited to illustrate that the proposed filtering strategy achieves better estimation accuracy than the conventional polynomial extended kalman filtering approach the effectiveness of the associated fault detection scheme is also demonstrated

graphical and numerical approach to robust stability analysis of fuzzy modeled systems with parametric uncertainty and disturbance abstract in this paper robust stability analysis methods for the fuzzy feedback control systems are presented the graphical method via multivariable circle criterion and the numerical method via linear matrix inequalities lmi the well known takagi sugeno fuzzy model is used as the nonlinear plant model uncertainties are assumed to be included in the model structure with known bounds for these structured parametric uncertainties l 2 robust stability analysis is performed by taking external disturbance as input and system state as output the effectiveness of the proposed methods is illustrated by examples

quadratic stability analysis of the takagi sugeno fuzzy model the nonlinear dynamic takagi sugeno fuzzy model with offset terms is analyzed as a perturbed linear system a sufficient criterion for the robust stability of this nominal system against nonlinear perturbations guarantees quadratic stability of the fuzzy model the criterion accepts a convex programming formulation of reduced computational cost compared to the common lyapunov matrix approach parametric robust control techniques suggest synthesis tools for stabilization of the fuzzy system application examples on fuzzy models of nonlinear plants advocate the efficiency of the method the examples demonstrate reduced conservatism compared to norm based criteria

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

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

brief paper set membership filtering for systems with sensor saturation this paper addresses the set membership filtering problem for a class of discrete time varying systems with sensor saturation in the presence of unknown but bounded process and measurement noises a sufficient condition for the existence of set membership filter is derived a convex optimisation method is proposed to determine a state estimation ellipsoid that is a set of states compatible with sensor saturation and unknown but bounded process and measurement noises a recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state by solving a time varying linear matrix inequality simulation results are provided to demonstrate the effectiveness of the proposed method

stability analysis and design of fuzzy control systems abstract the stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed first we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams we derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks in order to preserve some properties in a connection of fuzzy blocks continuous piecewise polynomial membership functions are defined secondly a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of lyapunov s direct method we give an important fact based on this condition thirdly we propose a new design technique of a fuzzy controller the fuzzy block diagrams and the stability analysis are applied to the design problems of a model based fuzzy controller

fuzzy model based control of complex plants in the field of fuzzy modeling the takagi sugeno fuzzy model has been used to approximate accurately the dynamics of complex plants the paper addresses two control design problems associated with state space realizations of such fuzzy models first we treat the stability robustness of fuzzy model based controllers against modeling uncertainty second we develop observer based control schemes and further investigate the behavior of estimated state feedback in both cases we provide sufficient conditions that guarantee stability of the closed loop the results are demonstrated on the fuzzy model of a gas furnace process

brief paper robust h filtering for nonlinear stochastic systems with uncertainties and markov delays this paper investigates the h filtering problem for a class of general nonlinear stochastic systems considering model uncertainties and random time delays governed by markov chains simultaneously the general algebraic hamilton jacobi inequality hji is established first for general nonlinear stochastic systems with uncertainties and random markovian delays then a set of sufficient conditions are derived for the filtering system to achieve stochastic stability in the sense of mean square and the prescribed disturbance attenuation level moreover the developed results are specialized for a special class of nonlinear stochastic systems by presenting the sufficient conditions in terms of matrix inequalities simulation results demonstrate the effectiveness of the proposed approach

event based h 2 h controllers for networked control systems this paper is concerned with event based h2 h control design for networked systems with interval time varying delays the contributions are twofold first conditions for uniform ultimately bounded stability are provided in the h2 h event based context the relation between the boundedness of the stability region and the threshold that triggers the events is studied second a practical design procedure for event based h2 h control is provided the method makes use of lyapunov krasovskii functionals lkfs and it is characterised by its generality as only mild assumptions are imposed on the structures of the lkf and the cost functional the robustness and performance of the proposed technique is showed through numerical simulations

relaxed stability and stabilization conditions for a t s fuzzy discrete system it is known that the stability condition of a t s fuzzy discrete system depends on the existence of the common matrix p which satisfies all lyapunov inequalities in general the common matrix p can be found by means of linear matrix inequalities lmi method however if the number of rules of a fuzzy system is large the common matrix p may not exist or may not be found even using lmi therefore in this paper the state space is divided into several subregions and the local common matrix p j for each subregion j is found then the number of lyapunov inequalities to be satisfied by the corresponding local common matrix p j becomes much fewer such that the stability condition of the fuzzy system is more relaxed the similar derivation is also extended to solve the stabilization problem of the t s fuzzy discrete system with parallel distributed compensation

optimal quasi optimal and superlinear band toeplitz preconditioners for asymptotically ill conditioned positive definite toeplitz systems in this paper we are concerned with the solution of n x n hermitian toeplitz systems with nonnegative generating functions f the preconditioned conjugate gradient pcg method with the well known circulant preconditioners fails in the case where f has zeros in this paper we consider as preconditioners band toeplitz matrices generated by trigonometric polynomials g of fixed degree l we use different strategies of approximation of f to devise a polynomial g which has some analytical properties of f is easily computable and is such that the corresponding preconditioned system has a condition number bounded by a constant independent of n for each strategy we analyze the cost per iteration and the number of iterations required for the convergence within a preassigned accuracy we obtain different estimates of l for which the total cost of the proposed pcg methods is optimal and the related rates of convergence are superlinear finally for the most economical strategy we perform various numerical experiments which fully confirm the effectiveness of approximation theory tools in the solution of this kind of linear algebra problems

fuzzy modeling and control of multilayer incinerator abstract a method to design a fuzzy controller based on a fuzzy model is suggested the identification method of a fuzzy model is also discussed the suggested method is applied for the modelling and control of a multilayer incinerator the performance of the designed fuzzy controller is tested by computer simulation using the identified process model the results are satisfactory

extended kalman filtering with stochastic nonlinearities and multiple missing measurements in this paper the extended kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon both deterministic and stochastic nonlinearities are included in the system model where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances the phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval 0 1 such a probability distribution is allowed to be any commonly used distribution over the interval 0 1 with known conditional probability the aim of the addressed filtering problem is to design a filter such that in the presence of both the stochastic nonlinearities and multiple missing measurements there exists an upper bound for the filtering error covariance subsequently such an upper bound is minimized by properly designing the filter gain at each sampling instant it is shown that the desired filter can be obtained in terms of the solutions to two riccati like difference equations that are of a form suitable for recursive computation in online applications an illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme

control laws for takagi sugeno fuzzy models abstract this work presents control laws for fuzzy models of takagi sugeno ts sugeno and kang fuzzy sets and systems 28 1988 15 33 takagi and sugeno ieee trans systems man cybernet 15 1 1985 116 132 these laws are not directly put in the classical form called pdc for parallel distributed compensation wang et al ieee trans fuzzy systems 4 1 1996 14 23 on a particular family of ts fuzzy models the approach allows to reduce the conservatism of results found in the literature tanaka et al ieee trans fuzzy systems 6 2 1998 1 16 this approach utilizes the state space representation according to that remark observers are also considered and a separation principle is also given though results are provided mainly for the continuous case they are also provided for the discrete case an example in both cases illustrates the results obtained

h filtering with randomly occurring sensor saturations and missing measurements in chap 5 the h filtering problem is studied for a class of nonlinear systems with randomly occurring incomplete information where the considered incomplete information includes both the sensor saturations and the missing measurements a new phenomenon of sensor saturation namely randomly occurring sensor saturation ross is first put forward in order to better reflect the reality in a networked environment then a novel sensor model is established to account for both the ross and missing measurement in a unified representation based on this sensor model a regional h filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean square asymptotically stable and the h norm requirement is satisfied

sliding mode filter design for nonlinear polynomial systems with unmeasured states this paper addresses the mean square and mean module filtering problems for a nonlinear polynomial stochastic system with gaussian white noises the obtained solutions contain a sliding mode term signum of the innovations process it is shown that the designed sliding mode mean square filter generates the mean square estimate which has the same minimum estimation error variance as the estimate given by the conventional mean square polynomial filter basin et al 2008 8 although the gain matrices of both filters are different the designed sliding mode mean module filter generates the mean module estimate which yields a better value of the mean module criterion in comparison to the conventional mean square polynomial filter the theoretical result is complemented with an illustrative example verifying performance of the designed filters it is demonstrated that the estimates produced by the designed sliding mode mean square filter and the conventional mean square polynomial filter yield the same estimation error variance and there is an advantage in favor of the designed sliding mode mean module filter

stochastic stability of the discrete time extended kalman filter the authors analyze the error behavior for the discrete time extended kalman filter for general nonlinear systems in a stochastic framework in particular it is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough this result is verified by numerical simulations for an example system

some theorems on linear positive operators and functionals and their applications abstract in this paper we prove two theorems concerning linear positive operators and functionals in a very general sense we show how these results can be used to derive new results in a wide range of applications from approximation theory to the numerical treatment of differential equations and to the numerical analysis connected with signals and images

convergence property of interval matrices and interval polynomials in association with robust control system design and analysis the hurwitz property of interval matrices and interval polynomials has recently been actively investigated however its discrete counterpart the convergence property has seemingly not been much discussed in this paper this property is studied in comparison with the hurwitz counterpart some conditions under which interval matrices or interval polynomials are convergent are derived

event triggered filtering and fault estimation for nonlinear systems with stochastic sensor saturations abstractthis paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and event triggered measurement transmissions an event triggered transmission scheme is proposed with hope to ease the traffic burden and improve the energy efficiency the measurements are subject to randomly occurring sensor saturations governed by bernoulli distributed sequences special effort is made to obtain an upper bound of the filtering error covariance in the presence of linearisation errors stochastic sensor saturations as well as event triggered transmissions a filter is designed to minimise the obtained upper bound at each time step by solving two sets of riccati like matrix equations and thus the recursive algorithm is suitable for online computation sufficient conditions are established under which the filtering error is exponentially bounded in mean square the applicability of the presented method is demonstrated by dealing with the fault estimation problem

identification of dynamic fuzzy models this paper discusses a new type of dynamic fuzzy model for the control of complex systems the fuzzy model has two maps one is a global rule map to describe the global approximation inference effects the other is the local rule map to describe the properties of the inference rule on the local area a simple example is given to show how the fuzzy model can be used to design a controller an identification method is described to identify the dynamic fuzzy model this includes the determination of the number of fuzzy rules the structure and parameters of the local rule maps and the estimation of the parameters in the membership functions

h2 and h fuzzy filters with memory for takagi sugeno discrete time systems abstract this paper is concerned with the problem of h 2 and h fuzzy filter design for takagi sugeno t s discrete time systems the novelty of the proposed method in the context of t s fuzzy systems is the inclusion of an arbitrary number of past information states and measurements in the structure of the filter producing a t s fuzzy filter with memory linear matrix inequality relaxations based on multi polynomial fuzzy lyapunov functions are proposed for the filter design providing filters that attain lower bounds for the h 2 and h performance criteria when compared to other approaches available in the fuzzy literature that employ memoryless filters the advantages of the proposed approach are illustrated by numerical examples