proximal point algorithm for generalized multivalued nonlinear quasi variational like inclusions in banach spaces in this paper we define a new notion of j h proximal mapping for a nonconvex lower semicontinuous h subdifferentiable proper functional in banach spaces the existence and lipschitz continuity of j h proximal mapping of a lower semicontinuous h subdifferentiable proper functional are proved by applying this notion we introduce and study generalized multivalued nonlinear quasi variational like inclusions in reflexive banach spaces and propose a proximal point algorithm for finding the approximate solutions of this class of variational inclusions the convergence criteria of the iterative sequences generated by our algorithm is discussed several special cases are also given

identification of composite local linear state space models using a projected gradient search an identification method is described to determine a weighted combination of local linear state space models from input and output data normalized radial basis functions are used for the weights

on the interpretation and identification of dynamic takagi sugeno fuzzy models dynamic takagi sugeno fuzzy models are not always easy to interpret in particular when they are identified from experimental data it is shown that there exists a close relationship between dynamic takagi sugeno fuzzy models and dynamic linearization when using affine local model structures which suggests that a solution to the multiobjective identification problem exists however it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes due to the multiobjective nature of the identification problem studied here special considerations must be made during model structure selection experiment design and identification in order to meet both objectives some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied these include constrained and regularized identification and locally weighted identification their usefulness in the present context is illustrated by examples

high stability awfm filter for signal restoration and its hardware design abstract an adaptive weighted fuzzy mean awfm filter with high stability is proposed for signal restoration in this paper awfm is an extension of weighted fuzzy mean wfm filter by linking a fuzzy detector and a dynamic selection procedure to wfm in order to overcome the drawback of wfm in fine signal preservation the fuzzy detector detects the amplitude of impulse noise which will be used as the argument of the dynamic selection procedure by referring to two fuzzy intervals and the wfm filtered outputs then the dynamic selection procedure applies four heuristic decision rules to determine the final filtering output awfm not only preserves the high performance of wfm on removing heavy additive impulse noise but also improves the performance of wfm on light additive impulse noise moreover it results in a high stability on the full range of noise occurrence probability compared with the other filters awfm exhibits better performance in the criteria of mean absolute error mae and mean square error mse on the subjective evaluation of those filtered images awfm also results in a higher quality of global restoration for the dedicated hardware implementation the kernel of awfm filter wfm is synthesized with the generic lr fuzzy cells which realize high speed fuzzy inference the hardware complexity is much simpler than the conventional median filter and simulation result exhibits that up to 6 6m pixels per second can be filtered by a wfm filter with small chip area

generalized variational inclusions and generalized resolvent equations in banach spaces in this paper we introduce and study generalized variational inclusions and generalized resolvent equations in real banach spaces it is established that generalized variational inclusion problems in uniformly smooth banach spaces are equivalent to fixed point problems we also establish a relationship between generalized variational inclusions and generalized resolvent equations by using nadler s fixed point theorem and resolvent operator technique for m accretive mappings in real banach spaces we propose an iterative algorithm for computing the approximate solutions of generalized variational inclusions the iterative algorithms for finding the approximate solutions of generalized resolvent equations are also proposed the convergence of approximate solutions of generalized variational inclusions and generalized resolvent equations obtained by the proposed iterative algorithms is also studied our results are new and represent a significant improvement of previously known results some special cases are also discussed

local model network identification with gaussian processes a bayesian gaussian process gp modeling approach has recently been introduced to model based control strategies the estimate of the variance of the predicted output is the most useful advantage of gps in comparison to neural networks nns and fuzzy models however the gp model is computationally demanding and nontransparent to reduce the computation load and increase transparency a local linear gp model network is proposed in this paper the proposed methodology combines the local model network principle with the gp prior approach a novel algorithm for structure determination and optimization is introduced which is widely applicable to the training of local model networks the modeling procedure of the local linear gp lgp model network is demonstrated on an example of a nonlinear laboratory scale process rig

multivalued quasi variational inclusions and multivalued accretive equations the purpose of this paper is to introduce and study a class of more general multivalued quasi variational inclusions in banach spaces by using the accretive operator technique we establish the equivalence between these kinds of multivalued quasi variational inclusions and a kind of multivalued accretive equations in banach space then invoking michael s selection theorem and nadler s theorem some existence theorems and iterative algorithms for solving this kind of multivalued quasi variational inclusions in banach spaces are established and suggested

synthesis of operational transconductance amplifier based analog fuzzy functional blocks and its application a synthesis of analog fuzzy functional blocks a complementary membership function circuit a maximum circuit a complementary maximum circuit and a defuzzifier circuit based on operational transconductance amplifiers ota s is presented the complementary membership function circuit and the maximum circuit are synthesized from the formulations using bounded difference operations the defuzzifier circuit is synthesized as the follower aggregation circuit composed of multiplier type ota s spice simulations showed that the proposed fuzzy functional blocks feature high speed operations and low power consumption the complementary membership function circuit the maximum circuit and the complementary maximum circuit were built with discrete components and commercially available ota s and the performances of these circuits were confirmed by experiments as an application a singleton fuzzy controller with 3 spl times 3 rules is synthesized using the proposed circuits the simulation of this controller showed that the inference speed of the order of 15 mrps rules per second is easily realizable

constructing narmax models using armax models this paper outlines how it is possible to decompose a complex non linear modelling problem into a set of simpler linear modelling problems local armax models valid within certain operating regimes are interpolated to construct a global narmax non linear narmax model knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models hence it should not be considered as a pure black box approach but as an approach that utilizes a limited amount of a priori system knowledge it is shown that a large class of non linear systems can be modelled in this way and indicated how to decompose the systems range of operation into operating regimes standard system identification algorithms can be used to identify the narmax model and several aspects of the system identification problem are discussed and illustrated by a simulation example

a prediction error and stepwise regression estimation algorithm for non linear systems the identification of non linear systems based on a narmax non linear autoregressive moving average model with exogenous inputs model representation is considered and a combined stepwise regression prediction error estimation algorithm is derived the stepwise regression routine determines the model structure by detecting significant terms in the model while the prediction error algorithm provides optimal estimates of the final model parameters implementation of the algorithms is discussed in detail and several simulated examples and industrial applications are included to illustrate that parsimonious models of non linear systems can be identified using the algorithm

designing sequential systems with fuzzy j k flip flops abstract we develop a designing framework for dealing with fuzzy computation using fuzzy j k flip flops in situations where some hardware implementation of integrated circuits utilizing ideas of fuzzy sets have already been introduced relevant design procedures become strongly required it should be stressed that they do not arise as a straightforward generalization of methods suitable for two valued digital systems but have to be formulated in a different way however they reduce to well known methods when boolean expressions are put into account the general structure of the j k flip flop described by set and reset equations is provided next a resulting excitation table is summarized different methods of designing the associated combinational part of the sequential system are studied

iterative process with errors of nonlinear equations involvingm accretive operators abstract let x be a uniformly smooth and uniformly convex banach space and t d t x x be an m accretive operator with the domain d t and the range r t for any given f x we prove that the mann and ishikawa type iterative sequences with errors converge strongly to the unique solution of the equation x tx f where t may not be lipschitz some related results deal with the convergence of mann and ishikawa type iterative sequences with errors for approximating a solution of a continuous dissipative operator equation

state dependent parameter models of non linear sampled data systems a velocity based linearization approach discrete time models with state dependent parameters are derived for non linear sampled data systems velocity based linearization of the continuous time system followed by integration over the sampling interval is used to represent the sampled data system by a discrete time state space model with state dependent parameters by reconstructing the state a quasi arx model is obtained the parameters of which are functions of past inputs and outputs as the velocity based linearization procedure does not involve suppression of higher order terms the state dependent state space model and the quasi arx description give exact representations of the non linear system

some problems and results in the study of nonlinear analysis this paper is presented as a survey of the latest and new results on some topics in the study of nonlinear analysis which were obtained by the author and some chinese mathematicians

investigations on the applicability of fuzzy interference abstract this paper investigates the applicability of some fuzzy implication operators which were evaluated in the literature and are considered to be very good operators we show that iin 1 1 a a b b has the best performance in the cases examined some new fuzzy implication operators are also investigated and found comparable in all cases to the operator given above

fuzzy model based control stability robustness and performance issues a nonlinear controller based on a fuzzy model of mimo dynamical systems is described and analyzed the fuzzy model is based on a set of arx models that are combined using a fuzzy inference mechanism the controller is a discrete time nonlinear decoupler which is analyzed both for the adaptive and the fixed parameter cases a detailed stability analysis is carried out and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty which may include modeling error and disturbances in addition bounds on the asymptotic and transient performance are given the main assumptions on the system and model are that they must not have strong nonminimum phase effects except time delay and the unstructured uncertainty must not be too large a simulation example illustrates some of the properties of the modeling method and model based control structure

iterative algorithm for finding approximate solutions to completely generalized strongly nonlinear quasivariational inequalities in this paper we study iterative algorithms for finding approximate solutions of completely generalized strongly nonlinear quasivariational inequalities which include as a special case some known results in this field our results are the extension and improvements of the results of siddiqi and ansari ding and zeng

multivariate adaptive regression splines a new method is presented for flexible regression modeling of high dimensional data the model takes the form of an expansion in product spline basis functions where the number of basis functions as well as the parameters associated with each one product degree and knot locations are automatically determined by the data this procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties unlike recursive partitioning however this method produces continuous models with continuous derivatives it has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables in addition the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions

a new class of completely generalized quasi variational inclusions in banach spaces in this paper a new notion of j proximal mapping for a nonconvex lower semicontinuous subdifferentiable proper functional on banach space is introduced the existence and lipschitz continuity of j proximal mapping of a lower semicontinuous subdifferentiable proper functional are proved by applying the concept we introduce and study a new class of completely generalized quasi variational inclusions in reflexive banach spaces a novel and innovative iterative algorithm for finding the approximate solutions is suggested and analyzed the convergence criteria of the iterative sequences generated by the new iterative algorithm is also given these algorithm and existence result generalize many known results under hilbert space setting in recent literature to reflexive banach spaces

an adaptive multi scale basis method for modelling discrete time nonlinear dynamical systems based on locally applicable autoregressive moving average with exogenous armax models this paper proposes two new adaptive modelling schemes for discrete time non linear dynamic systems based on the concept of a multiscale which originates in approximation theory a general result on the convergence of modelling based on the multiscale basis functions with a least square estimated is derived with the advantage of the multiresolution nature of the multiscale basis the new modelling scheme can be used to capture both the global and local characteristics of a non linear system over distinct scales the algorithms also provide a trade off between the model structure model size and the modelling error one of which emphasizes the modelling error with variable model structure whilst the other stresses a desired model size with better modelling error data based modelling examples are used to demonstrate the effectiveness of the multiscale modelling schemes with different multiscale basis

process modelling and fault diagnosis using fuzzy neural networks abstract process modelling and fault diagnosis using fuzzy neural networks are studied in this paper in fuzzy network based modelling the process operation is partitioned into several fuzzy operating regions within each region a local model of simple form for example a low order linear model is used to model the process the global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs a fuzzy neural network can be used to implement such a fuzzy model process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights process input output data are used to train the network through training membership functions of fuzzy operating regions are refined and local models are learnt this technique has been successfully applied to the modelling of ph dynamics in a continuous stirred tank reactor cstr the fuzzy network for fault diagnosis is obtained by adding a fuzzification layer to a conventional feed forward network the fuzzification layer converts the increments in on line measurements and controller outputs into three fuzzy sets increase steady and decrease the following layers then classify the symptoms represented by these fuzzy sets into various fault categories the fuzzification layer can compress training data and thereby ease training effort robustness of the diagnosis system is enhanced by adopting a fuzzy approach in representing abnormalities in the process the technique has been successfully applied to a cstr system

recurrent neuro fuzzy networks for nonlinear process modeling a type of recurrent neuro fuzzy network is proposed in this paper to build long term prediction models for nonlinear processes the process operation is partitioned into several fuzzy operating regions within each region a local linear model is used to model the process the global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs this modeling strategy utilizes both process knowledge and process input output data process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights process i o data are used to train the network network weights are such trained so that the long term prediction errors are minimized through training membership functions of fuzzy operating regions are refined and local models are learnt based on the recurrent neuro fuzzy network model a novel type of nonlinear model based long range predictive controller can be developed and it consists of several local linear model based predictive controllers local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions this control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model based predictive control the techniques have been successfully applied to the modeling and control of a neutralization process

algorithms of common solutions for variational inclusions mixed equilibrium problems and fixed point problems in this paper we present an iterative algorithm for finding a common element of the set of solutions of a mixed equilibrium problem and the set of fixed points of an infinite family of nonexpansive mappings and the set of a variational inclusion in a real hilbert space furthermore we prove that the proposed iterative algorithm has strong convergence under some mild conditions imposed on algorithm parameters

iterative learning control for non linear systems described by a blended multiple model representation this paper deals with the design of gain scheduling based iterative learning controllers for continuous time non linear systems described by a blended multiple model representation sufficient conditions guaranteeing the convergence of the infinity norm as well as the u norm of the tracking error are derived the effectiveness of the proposed control scheme is illustrated on an example of a non affine in input system

fuzzy network model based fuzzy state controller design the performance of model based controller design relies heavily on the quality and suitability of the utilized process model this contribution proposes a fuzzy network based nonlinear controller design methodology fuzzy networks are a model approach combining high approximation quality with high interpretability the input output i o models commonly used for identification are transformed to fuzzy state space models transferring and adjusting methods from linear state space theory a control concept consisting of a fuzzy state controller and an adaptive set point filter for nonlinear dynamic processes is deduced the capability of the method is demonstrated for a hydraulic drive

lazy learning for local modelling and control design this paper presents local methods for modelling and control of discrete time unknown non linear dynamical systems when only input output data are available we propose the adoption of lazy learning a memory based technique for local modelling the modelling procedure uses a query based approach to select the best model configuration by assessing and comparing different alternatives a new recursive technique for local model identification and validation is presented together with an enhanced statistical method for model selection a lso three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described in the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take the second is an indirect method inspired by self tuning regulators where recursive least squares estimation is replaced by a local approximator the third method combines the linearization provided by t

auxiliary principle for generalized mixed variational like inequalities abstract in this paper we use the auxiliary principle technique to prove the existence of a solution of some new classes of variational inequalities which are called the generalized mixed variational like inequalities this technique is used to analyze an iterative algorithm several special cases which can be obtained from our main result are also discussed

a perturbed algorithm for variational inclusions a perturbed method for solving a new class of variational inclusions is presented and a convergence result which includes as a special case some known results in this field is given

mann and ishikawa type perturbed iterative algorithms for generalized quasivariational inclusions abstract in this paper we introduce a class of generalized quasivariational inclusions and show its equivalence with a class of fixed point problems by making use of the properties of proximal maps using this equivalence we develop the mann and ishikawa type perturbed iterative algorithms for this class of generalized quasivariational inclusions further using fixed point techniques we prove the existence of solutions for the class of generalized quasivariational inclusions and discuss the convergence criteria for the perturbed algorithms our algorithms and results improve and generalize many known corresponding algorithms and results

hybrid system stability and robustness verification using linear matrix inequalities this paper addresses issues concerning exponential stability and robustness of hybrid systems stability conditions using lyapunov techniques are given the search for the lyapunov functions is formulated as a linear matrix inequality lmi problem for hybrid systems with affine as well as non linear vector fields it is shown how the lyapunov approach most advantageously also can be used to guarantee stability despite the presence of model uncertainties several examples are given to illustrate the theory

set valued resolvent equations and mixed variational inequalities in this paper we introduce and study a new class of variational inequalities which is called the generalized set valued mixed variational inequality the resolvent operator technique is used to establish the equivalence among generalized set valued variational inequalities fixed point problems and the generalized set valued resolvent equations this equivalence is used to study the existence of a solution of set valued variational inequalities and to suggest a number of iterative algorithms for solving variational inequalities and related optimization problems the results proved in this paper represent a significant refinement and improvement of the previously known results in this area

on φ strongly accretive mappings and some set valued variational problems abstract this paper shows that a continuous strongly accretive mapping on a real banach space is single valued and some recent results of set valued variational inclusions and inequalities are discussed

identification of non linear system structure and parameters using regime decomposition abstract an off line algorithm for empirical modeling and identification of non linear dynamic systems is presented the minimal input to the algorithm is a sequence of empirical data and the model order using this information the algorithm searches for an optimal model structure and parameters within a rich non linear model set the model representation is based on the interpolation of a number of simple local models where each local model has a limited range of validity but the local models yield a complete global model when interpolated the method is illustrated using simulated data

existence and convergence theorems for a class of multi valued variational inclusions in banach spaces abstract an existence theorem for a new class of multi valued variational inclusion problems is established in smooth banach spaces further it is shown that a sequence of a mann type iteration algorithm is strongly convergent to the solutions in this class of variational inclusion problems

analytic framework for blended multiple model systems using linear local models in this paper it is shown that the dynamics of a conventional type of blended multiple model system are only weakly related to the local models from which it is formed a novel class of velocity based blended multiple model systems is proposed for which the dynamics are directly related to the local models indeed the solution to the blended multiple model system locally to a specific operating point is approximated by the weighted linear combination of the solutions to the local models moreover in contrast to conventional blended multiple model systems the velocity based blended multiple model systems employs linear local models thereby providing a degree of continuity with established linear methods and consequently facilitating analysis and design

practical identification of narmax models using radial basis functions a wide class of discrete time non linear systems can be represented by the nonlinear autoregressive moving average narmax model with exogenous inputs this paper develops a practical algorithm for identifying narmax models based on radial basis functions from noise corrupted data the algorithm consists of an iterative orthogonal forward regression routine coupled with model validity tests the orthogonal forward regression routine selects parsimonious radial basistunc tion models while the model validity tests measure the quality of fit the modelling of a liquid level system and an automotive diesel engine are included to demonstrate the effectiveness of the identification procedure

completely generalized multivalued nonlinear co quasi variational like inclusions in this paper we introduce and study completely generalized multivalued nonlinear co quasi variational like inclusions by using j h proximal mapping 1 we propose an iterative algorithm for computing the approximate solutions of completely generalized multivalued nonlinear co quasi variational like inclusions problem we prove that approximate solutions obtained by the proposed algorithm converge to the exact solutions of completely generalized multivalued nonlinear co quasi variational like inclusions problem some special cases are also discussed

set valued variational inclusions in banach spaces the purpose of this paper is to introduce and study a class of set valued variational inclusions without compactness condition in banach spaces by using michael s selection theorem and nadler s theorem an existence theorem and an iterative algorithm for solving this kind of set valued variational inclusions in banach spaces are established and suggested

the reduction of interval type 2 lr fuzzy sets type reduction of interval type 2 it2 fuzzy sets is essential in conducting the type 2 fuzzy sets expressed with the resolution forms of it2 fuzzy sets several type reduction methods such as km ekm and centroid flow have been proposed these methods are relatively easy to implement but still computation intensive because they need to invoke an iterative switching point finding procedure this study derives a theorem and proposes a heuristic algorithm which can fast and accurately identify the minimum and maximum switching points of a piecewise smooth it2 fuzzy set it also demonstrates that it is easy to derive the close form expressions of the switching points of a piecewise smooth it2 fuzzy set if both of its upper and lower membership functions can be parameterized as lr fuzzy sets which are defined in this paper then the type reduction of piecewise smooth it2 fuzzy sets can be simplified to solve the close form expressions of their switching points in terms of lr parameters experiments on it2 fuzzy sets with various piecewise smooth membership functions including linear gaussian and hybrid shaped ones were made the results showed that the proposed type reduction method can obtain solutions which accurately approximate to the desired switching points with much lower computational overhead than the karnik mondel km and enhanced km ekm methods

fuzzy quasivariational inclusions in banach spaces the purpose of this paper is to introduce the concept of general fuzzy quasi variational inclusions and to study the existence problem and iterative approximation problem of solutions for some kinds of fuzzy quasivariational inclusions in banach spaces by using the resolvent operator technique nadler s fixed point theorem and new analytic technique some existence theorems of solutions and iterative approximation for solving this kind of fuzzy quasivariational inclusions are established the results presented in this paper are new which generalize improve and unify a number of recent results

on a new system of generalized mixed quasi variational like inclusions involving a η m accretive operators with applications in this paper we introduce a new and interesting system of generalized mixed quasi variational like inclusions with a h m accretive operators and relaxed cocoercive mappings which contains variational inequalities variational inclusions systems of variational inequalities systems of variational like inequalities and systems of variational inclusions in the literature as special cases by using the resolvent technique for the a h m accretive operators we prove the existence of solutions and the convergence of a new p step iterative algorithm for this system of generalized mixed quasi variational like inclusions in real q uniformly smooth banach spaces the results in this paper unifies extends and improves some known results in the literature

fuzzy system identification using an adaptive learning rule with terminal attractors a fuzzy model with an adaptive learning rule is proposed to approximate a class of nonlinear systems the proposed adaptive learning rule uses the concept of terminal attractors and guarantees that the identification is stable and fast convergence in finite time another subject of this paper is to determine the appropriate rule number of the fuzzy model required for a desired modeling error a learning algorithm is also introduced to identify the centers of membership functions of fuzzy outputs

generalized set valued variational inclusions and resolvent equations in this paper we establish the equivalence between the generalized set valued variational inclusions the resolvent equations and the fixed point problem using the resolvent operator technique this equivalence is used to suggest and analyze some iterative algorithms for solving the generalized set valued variational inclusions and related optimization problems

algebraic fuzzy flip flop circuits abstract algebraic fuzzy flip flop circuits in discrete and continuous mode are presented the algebraic fuzzy flip flop is one example of the general fuzzy flip flop concept which has been defined as an extension of the binary j k flip flop two types of the algebraic fuzzy flip flop which are reset type and set type are defined using complementation algebraic product and algebraic sum operations for fuzzy negation t norm and s norm respectively a unified equation of the reset type and set type of an algebraic fuzzy flip flop is derived for the purpose of realization of hardware circuits based on the equation two types of hardware circuits in discrete mode and continuous mode are constructed moreover the characteristics of various fuzzy flip flops presented previously are investigated such as min max fuzzy flip flop in both discrete and continuous mode and the algebraic fuzzy flip flop presented in this paper

generalized set valued variational inclusions in q uniformly smooth banach spaces abstract in this paper a class of generalized set valued variational inclusions in banach spaces are introduced and studied which include many variational inclusions studied by others in recent years by using some new and innovative techniques several existence theorems for the generalized set valued variational inclusions in q uniformly smooth banach spaces are established and some perturbed iterative algorithms for solving this kind of set valued variational inclusions are suggested and analyzed our results improve and generalize many known algorithms and results

algorithm for solving a new class of general mixed variational inequalities in banach spaces in this paper a new concept of h proximal mapping for a proper subdifferentiable functional which may not be convex on a banach space is introduced an existence and lipschitz continuity of the h proximal mapping are proved by using properties of the h proximal mapping a new class of general mixed variational inequalities is introduced and studied in banach spaces an existence theorem of solutions is established and a new iterative algorithm for solving the general mixed variational inequality is suggested a convergence criteria of the iterative sequence generated by the new algorithm is also given

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

multiobjective identification of takagi sugeno fuzzy models the problem of identifying the parameters of the constituent local linear models of takagi sugeno fuzzy models is considered in order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system two multiobjective identification algorithms are studied particular attention is paid to the analysis of conflicts between objectives and we show that such information can be easily computed from the solution of the multiobjective optimization this information is useful to diagnose the model and tune the weighting priorities of the multiobjective optimization moreover the result of the conflict analysis can be used as a constructive tool to modify the fuzzy model structure including membership functions in order to meet the multiple objectives simple illustrative examples as well as experimental results show the usefulness of the method

input output parametric models for non linear systems part ii stochastic non linear systems recursive input output models for non linear multivariate discrete time systems are derived and sufficient conditions for their existence are defined the paper is divided into two parts the first part introduces and defines concepts such as nerode realization multistructural forms and results from differential geometry which are then used to derive a recursive input output model for multivariable deterministic non linear systems the second part introduces several examples compares the derived model with other representations and extends the results to create prediction error or innovation input output models for non linear stochastic systems these latter models are the generalization of the multivariable arm ax models for linear systems and are referred to as narmax or non linear autoregressive moving average models with exogenous inputs

the mann and ishikawa iterative approximation of solutions to variational inclusions with accretive type mappings abstract the purpose of this paper is to introduce and study the existence of solutions and convergence of mann and ishikawa iterative processes for a class of variational inclusions with accretive type mappings in banach spaces the results presented in this paper extend and improve the corresponding results of 1 9

generalized multivalued quasi variational inequalities in this paper we introduce and study a new class of variational inequalities known as generalized multivalued quasi variational inequalities this class is the most general and unifying one using essentially the projection technique we establish the equivalence between this class and the implicit wiener hopf equations this equivalence is used to suggest a number of iterative algorithms for solving the generalized multivalued quasi variational inequalities the convergence analysis of these algorithms is also studied the results proved in this paper represent a significant improvement and refinement of the previously known results in this area

a hybrid learning scheme combining em and masmod algorithms for fuzzy local linearization modeling fuzzy local linearization fll is a useful divide and conquer method for coping with complex problems such as modeling unknown nonlinear systems from data for state estimation and control based on a probabilistic interpretation of fll the paper proposes a hybrid learning scheme for fll modeling which uses a modified adaptive spline modeling masmod algorithm to construct the antecedent parts membership functions in the fll model and an expectation maximization em algorithm to parameterize the consequent parts local linear models the hybrid method not only has an approximation ability as good as most neuro fuzzy network models but also produces a parsimonious network structure gain from masmod and provides covariance information about the model error gain from em which is valuable in applications such as state estimation and control numerical examples on nonlinear time series analysis and nonlinear trajectory estimation using fll models are presented to validate the derived algorithm

identification of non linear parametrically varying models using separable least squares in this paper a novel identification algorithm for a class of non linear possibly parameter varying models is proposed the algorithm is based on separable least squares ideas these models are given in the form of a linear fractional transformation lft where the forward part is represented by a conventional linear regression and the feedback part is given by a non linear map which can take into account scheduling variables available for measurement the non linear part of the model can be parameterized according to various paradigms like e g neural network nn or general nonlinear autoregressive exogenous narx models the estimation algorithm exploits the separability of the criterion used to estimate the parameters when using a nn it is possible the explicit computation of the frechet derivative needed to implement a separable least square algorithm

on the generalized implicit quasivariational inequalities abstract in the present paper we introduce and study a new class of generalized nonlinear implicit quasivariational inequalities for set valued mappings and construct some new iterative algorithms we prove the existence of solutions for this class of generalized nonlinear implicit quasivariational inequalities for set valued mappings without compactness and the convergence of iterative sequences generated by the algorithms we also study a new perturbed iterative algorithm for solving a class of general strongly nonlinear quasivariational inequalities

the mann process for perturbed m accretive operators in banach spaces this work was carried out while this author was visiting the university ofalabama in huntsville underthe nancial support by the lg yonam foundation 1998 and he would like to thank professor claudio h morales for his hospitality in the department of mathematics 0362 546x 01 see front matter 2001 elsevier science ltd all rights reserved pii s0362 546x 00 00115 2

on variational inclusion and common fixed point problems in hilbert spaces with applications abstract the purpose of this paper is to introduce a general iterative method for finding a common element of the solution set of quasi variational inclusion problems and of the common fixed point set of an infinite family of nonexpansive mappings in the framework hilbert spaces strong convergence of the sequences generated by the purposed iterative scheme is obtained

comparison of fuzzy reasoning methods l a zadeh e h mamdani and m mizumoto et al have proposed methods for fuzzy reasoning in which the antecedent involves a fuzzy conditional proposition if x is a then y is b with a and b being fuzzy concepts mizumoto et al have investigated the properties of their methods in the case of generalized modus ponens this paper deals with the properties of their methods in the case of generalized modus tollens and investigates the other new fuzzy reasoning methods obtained by introducing the implication rules of many valued logic systems finally the properties of syllogism and contrapositive are investigated under each fuzzy reasoning method

the solution existence of general variational inclusion problems we propose general variational inclusion problems which are slightly different from corresponding problems considered in several recent papers in the literature and show that they are advantageous sufficient conditions for the solution existence are established as applications we derive consequences for several special cases of variational inclusion problems quasioptimization problems equilibrium problems and implicit variational inequalities and show that they improve the results of some recent existing papers

generic lr fuzzy cells for fuzzy hardware synthesis a novel primitive cell structure for high performance hardware realization of fuzzy computations is proposed in this paper such a cell structure is called generic lr fuzzy cell because it is an integral unit that encapsulates an lr fuzzy set and a basic fuzzy operation such as implication or arithmetic operation based on the proposed cell structure we can develop two major kinds of fuzzy cell lr fuzzy implication cell and lr fuzzy arithmetic cell for the systematic synthesis of fuzzy application specific integrated circuits asics or general purposed fuzzy processors the fuzzy systems synthesized with lr fuzzy cells possess the characteristics of decentralized knowledge manipulations and massively parallel inference the system expandability and reconfigurability are also warrantable this paper emphasizes on the design and application of fuzzy implication cell the lr fuzzy implication cell is implemented with analog current mode technology by this technology an implication cell has the characteristics including small circuit area high performance low power dissipation etc moreover the implication cell manipulates continuous data so that the realization of a pure fuzzy system is possible in this paper the key circuit characteristics of fuzzy implication cell are evaluated in details and there are two cases fuzzy knowledge system and fuzzy mean filter implemented to confirm the effectiveness and efficiency of fuzzy hardware synthesis by the lr fuzzy cells

the studies of systems of variational inclusions problems and variational disclusions problems with applications abstract in this paper we study existence theorems of solutions for systems of variational inclusions problems and systems of variational disclusions problems from these existence results we establish existence theorems of solutions for systems of generalized vector quasiequilibrium problems and systems of quasioptimization problems

iterative approximations of fixed points and solutions for strongly accretive and strongly pseudo contractive mappings in banach spaces abstract in this article we prove some new convergence theorems of the ishikawa and mann iteration sequences for strongly accretive and strongly pseudo contractive mappings by using the new inequality and the new approximation methods our main results improve and extend the corresponding results of j math anal appl 151 1990 453 461 proc amer math soc 120 1994 545 551 j math appl 192 1995 502 518 j math anal appl 174 1993 441 449 acta appl math 32 1993 183 196 nonlinear anal tma 24 1995 981 987 j math anal appl 178 1993 9 21 proc amer math soc 125 6 1997 1705 1709

ishikawa iterative processes with mixed errors for nonlinear equations with perturbed m accretive mappings in banach spaces abstract the purpose of this paper is to study the convergence of the ishikawa and mann iterative sequences with mixed errors to approximate solutions of nonlinear operator equations with perturbed m accretive mappings in arbitrary banach spaces the results presented in this paper extend and improve some recent results in jung and morales nonlinear anal tma 46 2001 231 243 jung et al applied math comput 133 2002 389 406 and kartsatos and liu nonlinear anal tma 24 1995 469 492

representations of non linear systems the narmax model abstract input output representations of non linear discrete time systems are discussed it is shown that the narmax non linear autoregressive moving average with exogenous inputs model is a general and natural representation of non linear systems and contains as special cases several existing non linear models the problem of approximating non linear input output systems is also addressed and several properties of non linear models are highlighted using simple examples