analysis and design of fuzzy control system abstract a continuous time fuzzy control system which is composed of a fuzzy model and a fuzzy controller is proposed under certain conditions the proposed fuzzy control system can be regarded as a fuzzy pid control system the stability analysis of the fuzzy control system is given a sufficient condition to guarantee the stability of the proposed fuzzy control system is proposed in terms of lyapunov s method the results can be applied to the design of a fuzzy pid control system the design procedure can also be applied to the controller design with a non fuzzy model examples are given to illustrate the design of the proposed fuzzy control system

frequency domain approach to optimally robust residual generation and evaluation for model based fault diagnosis methods of residual generation and evaluation for model based fault diagnosis in uncertain linear dynamic systems are investigated with the aid of frequency domain approaches and h optimization techniques based on the factorization technique the construction of residual generators in terms of a parametrization concept is proposed with its help the fault detection and isolation problem with perfect model uncertainty decoupling is studied and moreover by its optimal approximation the problem of optimally robust residual generation is treated the optimization problem is solved by h techniques to increase the robustness of residual evaluation a frequency domain residual evaluation index is introduced and optimal input adaptive fault thresholds are derived with respect to the frequency domain evaluation index the results are illustrated in terms of an inverted pendulum example

fault detection for uncertain fuzzy systems an lmi approach this paper studies the problem of designing a robust fault detection system for uncertain takagi sugeno fuzzy models the worst case fault sensitivity measure is formulated in terms of linear matrix inequalities the existence of a robust fault detection system that guarantees i the l2 gain from a fault signal to a residual signal greater than a prescribed value and ii the l2 gain from an exogenous input to a residual signal less than a prescribed value is given in terms of the solvability of linear matrix inequalities numerical examples are used to illustrate the effectiveness of the proposed design techniques

event triggered fuzzy h control for a class of nonlinear networked control systems using the deviation bounds of asynchronous normalized membership functions this paper is concerned with the event triggered h control for a nonlinear networked control system a new event triggered transmission scheme is first proposed to reduce the network bandwidth utilization under this scheme the nonlinear networked control system is formulated as a takagi sugeno fuzzy threshold error dependent system with asynchronous normalized membership functions then by using the deviation bounds of asynchronous normalized membership functions some sufficient conditions on the existence of the parameters of the event triggered transmission scheme and the fuzzy controller are derived such that the takagi sugeno fuzzy threshold error dependent system with asynchronous normalized membership functions is asymptotically stable with a prescribed h attenuation level it is shown that the proposed design method can reduce the conservativeness of some existing results finally two examples are given to demonstrate the effectiveness of the proposed design method and the event triggered transmission scheme

novel stability criteria for t s fuzzy systems in this paper novel stability conditions for takagi sugeno t s fuzzy systems are presented the so called nonquadratic membership dependent lyapunov function is first proposed which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature then new membership dependent stability conditions are developed by the new lyapunov function approach it is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the lyapunov function increases two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results

fuzzy filter design for ito stochastic systems with application to sensor fault detection the paper deals with the robust fault detection problem for takagi sugeno t s fuzzy ito stochastic systems our aim is to develop a robust fault detection approach to the t s fuzzy systems with brownian motion by using a general observer based fault detection filter as a residual generator the robust fault detection is formulated as a filtering problem attention is focused on the design of both the fuzzy rule independent and the fuzzy rule dependent fault detection filters guaranteeing a prescribed noise attenuation level in an hinfin sense sufficient conditions are proposed to guarantee the mean square asymptotic stability with an hinfin performance for the fault detection system the corresponding solvability conditions for the desired fuzzy rule independent and fuzzy rule dependent fault detection filters are also established finally a numerical example is provided to illustrate the effectiveness of the proposed theory

stabilizing fuzzy system models using linear controllers a lyapunov based approach is used to derive a sufficiency condition for stabilizing a class of fuzzy system models using linear controllers a design algorithm for constructing stabilizing linear controllers is given the results obtained are illustrated with a design of a linear stabilizing controller for a system consisting of an inverted pendulum mounted on a cart the controller is designed using a fuzzy model of the system and tested on the original system model

learning a multiple kernel similarity metric for kinship verification abstract kinship verification kv has recently caught much attention in the computer vision community due to its potential applications ranging from missing children search to social media analysis most of the related work focuses either on developing hand crafted feature representations to describe the faces or on learning the mahalanobis distance metric to measure the similarity between facial images instead in this paper we propose a novel multiple kernel similarity metric mksm in which different from the mahalanobis metric the similarity computation is essentially based on an implicit nonlinear feature transformation the overall mksm is a weighted combination of basic similarities and therefore possesses the capacity for feature fusion the basic similarities are derived from base kernels and local features and the weights are obtained by solving a constrained linear programming lp problem that originates from a large margin lm criterion particularly the lm criterion not only guarantees the generalization on unseen samples when the training set is small but also leads to sparsity in the weight vector which in turn boosts the efficiency at the prediction stage extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed method

dimensionality reduction by feature clustering for regression problems one of the issues encountered in classification and regression is the processing inefficiency caused by a large number of input dimensions involved in the given training data set many dimensionality reduction approaches have been proposed to address this issue by reducing the number of input dimensions and maintaining the generalization capability of the original data set however less attention has been paid to regression than to classification besides the computation with covariance matrices involved results in an inefficient reduction process in most existing methods in this paper we propose a machine learning based dimensionality reduction approach for regression problems for a given set of training instances a group of clusters are formed such that the instances included in the same cluster are similar to each other then one new feature is extracted from each cluster through a certain weighted combination of the training instances consequently the dimensionality of the original data set is reduced the clusters are created incrementally and automatically without the need of specifying the number of clusters in advance by the user the characteristics of the original data set are substantially retained since all the original features are involved in the derivation of the extracted features also the computation with covariance matrices is avoided and thus efficiency is maintained a number of experiments on real world data sets are conducted to demonstrate the effectiveness of the proposed approach

fault diagnosis of dynamical systems using recurrent fuzzy systems with application to an electrohydraulic servo axis this paper presents a novel approach for fault isolation based on discrete time recurrent fuzzy systems dtrfs extending the well known static fuzzy systems for fault isolation the dtrfs additionally allows for the isolation of faults causing dynamic residuals and the isolation of multiple subsequently occurring faults due to the flexibility of the dtrfs multiplicative as well as additive faults can be handled the approach is applied to an electrohydraulic servo axis with a duplex valve system experimental results at the testbed underline the effectiveness and applicability of the approach and show the enhanced performance compared to static fuzzy systems and state automata

finite time h_ infty filtering for t s fuzzy discrete time systems with time varying delay and norm bounded uncertainties in this paper we investigate the filtering problem of discrete time takagi sugeno t s fuzzy uncertain systems subject to time varying delays a reduced order filter is designed with the augmentation technique a filtering error system with delayed states is obtained in order to deal with time delays in system states the filtering error system is first transformed into two interconnected subsystems by using a two term approximation for the time varying delay sufficient delay dependent conditions of finite time boundedness and h infty performance of the filtering error system are derived with the lyapunov function based on these conditions the filter design methods are proposed and the filter gain matrices can be obtained by calculating a set of linear matrix inequalities a numerical example is used to illustrate the effectiveness of the proposed approaches

h output feedback control of fuzzy system models under sampled measurements this paper studies the problem of control for a class of fuzzy system models by using a linear output controller the system under consideration is described by a continuous time fuzzy system model under sampled output measurements the premise variables of the fuzzy system model are assumed unmeasurable we develop a technique for designing an output feedback control law which globally stabilises this class of fuzzy system models a design algorithm for constructing the linear output controller is given a numerical simulation example is given to show the potential of the proposed techniques

a new fuzzy compactness defined by fuzzy nets compactness is one of the most important notions in topology and is thoroughly investigated in general topology but it seems that the research about this notion in the area of fuzzy topology is not so effective as in general topology in fact it would seem that there was not even a good definition of compactness in this area until recently indeed soon after the famous article written by zadeh 8 was published chang 1 introduced the notion of fuzzy topological spaces and at the same time a certain fuzzy compactness was given from then on a series of different notions of fuzzy compactness have been discussed 2 3 71 and in 5 they were compared in detail but it seems unsatisfactory that all these fuzzy compactness ideas are defined for the whole fuzzy topological space but not for an arbitrary fuzzy subset therefore they are far from the crisp compactness for example it is impossible to discuss the hereditary property with respect to closed subsets of fuzzy compactness in 1977 pu and liu introduced the notion of fuzzy nets and a new concept of so called q neighborhood was given which could reflect the features of neighborhood structure in fuzzy topological spaces and by this new neighborhood structure the moore smith convergence theory was established spendidly 6 it furnishes us a basic tool to investigate the fuzzy compactness in a more general way to investigate the compactness of arbitrary fuzzy subsets in this paper a new fuzzy compactness defined by fuzzy nets we shall call it n compactness is given but we introduce the notion of r neighborhood see section 2 instead of qneighborhood to establish the moore smith convergence theory for it is perhaps more convennient to use n compactness possesses the following properties 1 n compactness is defined for arbitrary fuzzy subsets 2 for n compactness the hereditary property with respect to closed subsets holds 3 n compactness is a good extension 5 4 the continuous images of ncompact sets are n compact 5 for n compactness the tychonoff product theorem holds 6 if an n compact fuzzy topological space is t2 6 then it will satisfy a stronger separation axiom which will be called t4 in this paper 1 0022 247x 83 3 00

stability analysis and synthesis of fuzzy singularly perturbed systems in this paper we investigate the stability analysis and synthesis problems for both continuous time and discrete time fuzzy singularly perturbed systems for continuous time case both the stability analysis and synthesis can be parameterized in terms of a set of linear matrix inequalities lmis for discrete time case only the analysis problem can be cast in lmis while the derived stability conditions for controller design are nonlinear matrix inequalities nmis furthermore a two stage algorithm based on lmi and iterative lmi ilmi techniques is developed to solve the resulting nmis and the stabilizing feedback controller gains can be obtained for both continuous time and discrete time cases the reduced control law which is only dependent on the slow variables is also discussed finally an illustrated example based on the flexible joint inverted pendulum model is given to illustrate the design procedures

stabilizing controller design for uncertain nonlinear systems using fuzzy models a lyapunov based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed the controller is constructed using a design model of the dynamical process to be controlled the design model is obtained from the truth model using a fuzzy modeling approach the truth model represents a detailed description of the process dynamics the truth model is used in a simulation experiment to evaluate the performance of the controller design a method for generating local models that constitute the design model is proposed sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state feedback controllers are given the results obtained are illustrated with a numerical example involving a four dimensional nonlinear model of a stick balancer

technical communique fuzzy h output feedback control of nonlinear systems under sampled measurements this paper studies the problem of designing an h fuzzy feedback control for a class of nonlinear systems described by a continuous time fuzzy system model under sampled output measurements the premise variables of the fuzzy system model are allowed to be unavailable we develop a technique for designing an h fuzzy feedback control that guarantees the l 2 gain from an exogenous input to a controlled output is less than or equal to a prescribed value a design algorithm for constructing the h fuzzy feedback controller is given

starplus compactness and starplus compact open fuzzy topologies on function spaces extending lowen s notion of strong fuzzy compactness to an arbitrary fuzzy set the notion of a starplus compact fuzzy set is introduced it is shown that the category of starplus compact fuzzy topological spaces is productive and that starplus compactness is a good extension of the notion of compactness it is shown that the class of starplus compact fuzzy sets is pseudo closed hereditary and invariant under fuzzy continuous maps moreover the notion of starplus compact open fuzzy topology on a function space is introduced and its interrelations with the fuzzy topology of pointwise convergence and the fuzzy topology of joint fuzzy continuity are studied it is shown that a fuzzy topology on a function space which is jointly fuzzy continuous on starplus compacta is finer than the starplus compact open fuzzy topology sufficient conditions on function spaces are obtained for the starplus compact open fuzzy topology and the fuzzy topology of joint fuzzy continuity on starplus compacta to coincide

function spaces in fuzzy topology abstract function spaces in several categories of fuzzy topological spaces and fuzzy continuous maps are studied the approach is via the existence of splitting and conjoining structures and makes extensive use of the theory of monotopological categories

a generalised form of compactness in fuzzy topological spaces the n compactness of wang 13 extended in 151 provides the first theory of compactness which is applicable to arbitrary fuzzy subsets many of the standard properties associated with compactness are valid for n compactness including the product theorem furthermore n compactness reduces to ordinary compactness in the topologically generated case on closer inspection one finds evidence that a concept weaker than n compactness might also be desirable for example n compactness does not reduce to any of the standard fuzzified versions of compactness 1 2 6 73 in the case where the fuzzy subset under consideration is the whole space in particular when applied to the whole space n compactness is substantially stronger than fuzzy compactness as fuzzy compactness seems to be a good working concept for the whole space an extension of this notion to arbitrary fuzzy subsets might well have applications not accessible to the much stronger n compactness as a consequence of the definition an n compact fuzzy set is forced to attain a maximum value as a result it is possible to have fuzzy sets which are never n compact even if the fuzzy topology has only a finite number of open fuzzy sets our intuition based on the case of general topology would expect every fuzzy set to be compact in such a case in this paper we introduce a weaker generalisation of compactness which we call f compactness it is shown that ficompactness is inherited by closed fuzzy subsets continuous images finite suprema and products it reduces to compactness or to fuzzy compactness in all cases where

robust h control for a class of discrete time fuzzy systems via delta operator approach in this paper we investigate a robust h control problem for a class of t s fuzzy systems with time delays by using delta operator approach it is known that a better control effect can be obtained by using delta operator approach than using shift operator approach for small sampling periods furthermore the delta operator can unify some previous related continuous and discrete fuzzy systems into fuzzy delta operator system framework based on lyapunov krasovskii functionals in delta domain a new fuzzy h state feedback controller is presented in terms of linear matrix inequalities some experiment results of an ball and beam model on a laboratory scale setup are presented to illustrate the effectiveness and potential for the developed techniques

fault detection for fuzzy systems with intermittent measurements this paper investigates the problem of fault detection for takagi sugeno t s fuzzy systems with intermittent measurements the communication links between the plant and the fault detection filter are assumed to be imperfect i e data packet dropouts occur intermittently which appear typically in a network environment and a stochastic variable satisfying the bernoulli random binary distribution is utilized to model the unreliable communication links the aim is to design a fuzzy fault detection filter such that for all data missing conditions the residual system is stochastically stable and preserves a guaranteed performance the problem is solved through a basis dependent lyapunov function method which is less conservative than the quadratic approach the results are also extended to t s fuzzy systems with time varying parameter uncertainties all the results are formulated in the form of linear matrix inequalities which can be readily solved via standard numerical software two examples are provided to illustrate the usefulness and applicability of the developed theoretical results

h sub spl infin fuzzy output feedback control design for nonlinear systems an lmi approach addresses the problem of stabilizing a class of nonlinear systems by using an h sub spl infin fuzzy output feedback controller first a class of nonlinear systems is approximated by a takagi sugeno ts fuzzy model then based on a well known lyapunov functional approach we develop a technique for designing an h sub spl infin fuzzy output feedback control law which guarantees the l sub 2 gain from an exogenous input to a regulated output is less or equal to a prescribed value a design algorithm for constructing an h sub spl infin fuzzy output feedback controller is given in contrast to the existing results the premise variables of the h sub spl infin fuzzy output feedback controller are not necessarily to be the same as the premise variables of the ts fuzzy model of the plant a numerical simulation example is presented to illustrate the theory development

learning the kernel matrix by maximizing a kfd based class separability criterion the advantage of a kernel method often depends critically on a proper choice of the kernel function a promising approach is to learn the kernel from data automatically in this paper we propose a novel method for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by linear discriminant analysis lda and kernel fisher discriminant kfd it is interesting to note that optimizing this criterion function does not require inverting the possibly singular within class scatter matrix which is a computational problem encountered by many lda and kfd methods we have conducted experiments on both synthetic data and real world data from uci and feret showing that our method consistently outperforms some previous kernel learning methods

an approach to fuzzy control of nonlinear systems stability and design issues presents a design methodology for stabilization of a class of nonlinear systems first the authors represent a nonlinear plant with a takagi sugeno fuzzy model then a model based fuzzy controller design utilizing the concept of the so called parallel distributed compensation is employed the main idea of the controller design is to derive each control rule so as to compensate each rule of a fuzzy system the design procedure is conceptually simple and natural moreover the stability analysis and control design problems can be reduced to linear matrix inequality lmi problems therefore they can be solved efficiently in practice by convex programming techniques for lmis the design methodology is illustrated by application to the problem of balancing and swing up of an inverted pendulum on a cart

constrained infinite horizon model predictive control for fuzzy discrete time systems the problem of constrained infinite horizon model predictive control for fuzzy discrete systems is considered in this paper new sufficient conditions are proposed in terms of linear matrix inequalities based on the optimal solutions of these sufficient conditions at each sampling instant we design both parallel distributed compensation and nonparallel distributed compensation state feedback controllers which can guarantee that the resulting closed loop fuzzy discrete system is asymptotically stable in addition the fuzzy feedback controllers meet the specifications for the fuzzy discrete systems with both input and output constraints numerical examples are presented to demonstrate the effectiveness of the proposed techniques

robust stabilization design of nonlinear stochastic partial differential systems fuzzy approach abstract in this paper we address the robust stabilization design problem for nonlinear stochastic partial differential systems nspdss in the presence of random external disturbances and measurement noise in the spatio temporal domain we propose a fuzzy stochastic partial differential system to approximate the nspdss based on fuzzy interpolation approach next we develop a fuzzy stochastic spatial state space model to represent the fuzzy stochastic partial differential system by employing a semi discretization finite difference scheme based on this model we propose a robust fuzzy estimator based stabilization controller to stabilize the nspdss furthermore robust stochastic h stabilization design is suggested to attenuate the effects of random external disturbances and measurement noise in the spatio temporal domain from the area energy point of view the lmi technique is applied to solve the issue of control gains and estimator gains of the controller by using a systematic control design procedure finally a simulation is presented as an example to illustrate the design procedure and to confirm the performance of the proposed robust fuzzy estimator based stabilization design for the nspdss

localized algorithms for multiple kernel learning instead of selecting a single kernel multiple kernel learning mkl uses a weighted sum of kernels where the weight of each kernel is optimized during training such methods assign the same weight to a kernel over the whole input space and we discuss localized multiple kernel learning lmkl that is composed of a kernel based learning algorithm and a parametric gating model to assign local weights to kernel functions these two components are trained in a coupled manner using a two step alternating optimization algorithm empirical results on benchmark classification and regression data sets validate the applicability of our approach we see that lmkl achieves higher accuracy compared with canonical mkl on classification problems with different feature representations lmkl can also identify the relevant parts of images using the gating model as a saliency detector in image recognition problems in regression tasks lmkl improves the performance significantly or reduces the model complexity by storing significantly fewer support vectors

optimizing the gaussian kernel function with the formulated kernel target alignment criterion for two class pattern classification nowadays most of the current kernel learning approaches are showing good results in small datasets and fail to scale to large ones as such it is necessary to develop faster kernel optimization algorithms that perform better with larger datasets especially for the big data applications this paper presents a novel fast method to optimize the gaussian kernel function for two class pattern classification tasks where it is desirable for the kernel machines to use an optimized kernel that adapts well to the input data and the learning tasks we propose to optimize the gaussian kernel function by using the formulated kernel target alignment criterion by adopting the euler maclaurin formula and the local and global extremal properties of the approximate kernel separability criterion the approximate criterion function can be proved to have a determined global minimum point thus when the approximate criterion function is a sufficient approximation of the criterion function through using a newton based algorithm the proposed optimization is simply solved without being repeated the searching procedure with different starting points to locate the best local minimum the proposed method is evaluated on thirteen data sets with three gaussian kernel based learning algorithms the experimental results show that the criterion function has the determined global minimum point for the all thirteen data sets the proposed method achieves the best high time efficiency performance and the best overall classification performance

analysis and design of fuzzy controller and fuzzy observer this paper addresses the analysis and design of a fuzzy controller and a fuzzy observer on the basis of the takagi sugeno t s fuzzy model the main contribution of the paper is the development of the separation property that is the fuzzy controller and the fuzzy observer can be independently designed a numerical simulation and an experiment on an inverted pendulum system are described to illustrate the performance of the fuzzy controller and the fuzzy observer

comments on robust stabilization of a class of uncertain nonlinear systems via fuzzy control quadratic stabilizability h sup spl infin control theory and linear matrix inequalities this paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems first a stability condition for takagi and sugeno s fuzzy model is given in terms of lyapunov stability theory next new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization h sup spl infin control theory and linear matrix inequalities the derived stability conditions are used to analyze the stability of takagi and sugeno s fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems the design method employs the so called parallel distributed compensation important issues for the stability analysis and design are remarked finally three design examples of fuzzy controllers for stabilizing nonlinear systems and uncertain nonlinear systems are presented

a new lmi based approach to relaxed quadratic stabilization of t s fuzzy control systems this paper proposes a new quadratic stabilization condition for takagi sugeno t s fuzzy control systems the condition is represented in the form of linear matrix inequalities lmis and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature a rigorous theoretic proof is given to show that the proposed condition can include previous results as special cases in comparison with conventional conditions the proposed condition is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design the latter design work is quite hard for t s fuzzy control systems based on the lmi based conditions derived one can easily synthesize controllers for stabilizing t s fuzzy control systems since only a set of lmis is involved the controller design is quite simple and numerically tractable finally the validity and applicability of the proposed approach are successfully demonstrated in the control of a continuous time nonlinear system

mixed h sub 2 h sub spl infin fuzzy output feedback control design for nonlinear dynamic systems an lmi approach this study introduces a mixed h sub 2 h sub spl infin fuzzy output feedback control design method for nonlinear systems with guaranteed control performance first the takagi sugeno fuzzy model is employed to approximate a nonlinear system next based on the fuzzy model a fuzzy observer based mixed h sub 2 h sub spl infin controller is developed to achieve the suboptimal h sub 2 control performance with a desired h sub spl infin disturbance rejection constraint a robust stabilization technique is also proposed to override the effect of approximation error in the fuzzy approximation procedure by the proposed decoupling technique and two stage procedure the outcome of the fuzzy observer based mixed h sub 2 h sub spl infin control problem is parametrized in terms of the two eigenvalue problems evps one for observer and the other for controller the evps can be solved very efficiently using the linear matrix inequality lmi optimization techniques a simulation example is given to illustrate the design procedures and performances of the proposed method

fault detection for t s fuzzy systems with partly unmeasurable premise variables abstract this paper studies the fault detection problem for t s fuzzy systems with partly unmeasurable premise variables by using measurable premise variables and estimations of unmeasurable premise variables of fuzzy models as the premise variables of observers a novel fuzzy fault detection observer scheme is constructed and the corresponding design condition is proposed via a set theoretic description different from the conventional fault detection observer schemes where all premise variables of the observer are the estimations of the premise variables in the fuzzy systems the proposed method can take full advantage of the partly measurable premise variables for achieving a better fault detection performance a numerical example is given to illustrate 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

enhancement of robustness in observer based fault detection a prerequisite for the feasibility of the technique of observer based fault detection and isolation fdi in dynamic systems is a satisfactory robustness with respect to modelling uncertainties this paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation among these methods are the generalized observer scheme the robust parity space check the unknown input and h observer scheme the decorrelation filter and the concept of adaptive threshold selection it is pointed out that the unknown input observer concept which provides perfect decoupling from the modelling errors or its optimal approximation with the aid of h techniques constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches among them the parity space and detection filter approach it is further shown that this fdi method can even be applied to a certain class of nonlinear systems

feature extraction using a fast null space based linear discriminant analysis algorithm the small sample size problem is often encountered in pattern recognition several algorithms for null space based linear discriminant analysis nlda have been developed to solve the problem however these algorithms for nlda have high computational cost in this paper we simplify the recently proposed algorithm for nlda in chu and thye 2010 5 with the assumption that all the training data vectors are linearly independent and propose a new and fast algorithm for nlda our main observation is that two steps of economic qr decomposition with column pivoting can be replaced by one step of economic qr decomposition without column pivoting if the related matrix is of full column rank the main features of our algorithm for nlda include i our nlda algorithm is carried out by only one step of economic qr decomposition and does not compute any singular value decomposition svd when all the training data vectors are linearly independent ii the main cost of our method is the cost of an economic qr decomposition of an mx n 1 matrix here m is the dimension of the training data and n is the number of samples our method is a fast one experimental studies on orl feret and pie face databases demonstrate the effectiveness of our new algorithm for nlda

optimally robust redundancy relations for failure detection in uncertain systems all failure detection methods are based either explicitly or implicitly on the use of redundancy i e on possibly dynamic relations among the measured variables the robustness of the failure detection process consequently depends to a great degree on the reliability of the redundancy relations which in turn is affected by the inevitable presence of model uncertainties in this paper the problem of determining redundancy relations that are optimally robust is addressed in a sense that includes several major issues of importance in practical failure detection and that provides a significant amount of intuition concerning the geometry of robust failure detection a procedure is given involving the construction of a single matrix and its singular value decomposition for the determination of a complete sequence of redundancy relations ordered in terms of their level of robustness this procedure also provides the basis for comparing levels of robustness in redundancy provided by different sets of sensors

model approximation for fuzzy switched systems with stochastic perturbation in this paper the model approximation problem is investigated for a takagi sugeno fuzzy switched system with stochastic disturbance for a high order considered system our attention is focused on the construction of a reduced order model which not only approximates the original system well with a hankel norm performance but translates it into a lower dimensional fuzzy switched system as well by using the average dwell time approach and the piecewise lyapunov function technique a sufficient condition is first proposed to guarantee the mean square exponential stability with a hankel norm error performance for the error system the model approximation is then converted into a convex optimization problem by using a linearization procedure finally simulations are provided to illustrate the effectiveness of the proposed theory

multiclass linear dimension reduction by weighted pairwise fisher criteria we derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well known k class fisher criterion associated with linear discriminant analysis lda it can be seen that lda weights contributions of individual class pairs according to the euclidean distance of the respective class means we generalize upon lda by introducing a different weighting function

h sub spl infin filtering for fuzzy singularly perturbed systems with pole placement constraints an lmi approach this paper addresses the problem of designing an h sub spl infin filter for a class of nonlinear singularly perturbed systems described by a takagi sugeno ts fuzzy model based on a linear matrix inequality lmi approach we develop a fuzzy h sub spl infin controller that guarantees that i the l sub 2 gain from an exogenous input to a filter error is less than or equal to a prescribed value and ii the poles of each local filter are within a prespecified region in order to alleviate the ill conditioning resulting from the interaction of slow and fast dynamic modes solutions to the problem are given in terms of linear matrix inequalities which are independent of the singular perturbation spl epsiv when spl epsiv is sufficiently small the proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard but also to nonstandard singularly perturbed nonlinear systems a numerical example is provided to illustrate the design developed in this paper

h h fault detection filter design for discrete time takagi sugeno fuzzy system abstract in this note a robust fault detection observer is designed for a t s takagi sugeno fuzzy model with sensor faults and unknown bounded disturbances the method applies the technique of descriptor systems by considering sensor faults as an auxiliary state variable the idea is to formulate the robust fault detection observer design as an h h problem based on nonquadratic lyapunov functions a solution of the considered problem is then given via a linear matrix inequality lmi formulation an example is proposed to illustrate the design conditions

limitations of nonlinear pca as performed with generic neural networks kramer s 1991 nonlinear principal components analysis nlpca neural networks are feedforward autoassociative networks with five layers the third layer has fewer nodes than the input or output layers this paper proposes a geometric interpretation for kramer s method by showing that nlpca fits a lower dimensional curve or surface through the training data the first three layers project observations onto the curve or surface giving scores the last three layers define the curve or surface the first three layers are a continuous function which we show has several implications nlpca projections are suboptimal producing larger approximation error nlpca is unable to model curves and surfaces that intersect themselves and nlpca cannot parameterize curves with parameterizations having discontinuous jumps we establish results on the identification of score values and discuss their implications on interpreting score values we discuss the relationship between nlpca and principal curves and surfaces another nonlinear feature extraction method

linear dimensionality reduction via a heteroscedastic extension of lda the chernoff criterion we propose an eigenvector based heteroscedastic linear dimension reduction ldr technique for multiclass data the technique is based on a heteroscedastic two class technique which utilizes the so called chernoff criterion and successfully extends the well known linear discriminant analysis lda the latter which is based on the fisher criterion is incapable of dealing with heteroscedastic data in a proper way for the two class case the between class scatter is generalized so to capture differences in co variances it is shown that the classical notion of between class scatter can be associated with euclidean distances between class means from this viewpoint the between class scatter is generalized by employing the chernoff distance measure leading to our proposed heteroscedastic measure finally using the results from the two class case a multiclass extension of the chernoff criterion is proposed this criterion combines separation information present in the class mean as well as the class covariance matrices extensive experiments and a comparison with similar dimension reduction techniques are presented

optimal learning rates of l p type multiple kernel learning under general conditions one of the most promising learning kernel methods is the l p type multiple kernel learning proposed by kloft et al 2009 this method can adaptively select kernel function in supervised learning problems the majority of the studies associated with generalization error have recently received wide attention in machine learning and statistics the present study aims to establish a new generalization error bound under more general frameworks in which the correlation among reproducing kernel hilbert spaces rkhss is considered and the restriction of smooth condition on the target function is relaxed in this case the interaction between the estimation and approximation errors must be simultaneously regarded in this investigation optimal learning rates are derived by applying the local rademacher complexity technique which is given in terms of the capacity of rkhss spanned by multi kernels and target function regularity

evolutionary combination of kernels for nonlinear feature transformation abstract the performance of kernel based feature transformation methods depends on the choice of kernel function and its parameters in addition most of these methods do not consider the classification information and error for the mapping features in this paper we propose to determine a kernel function for kernel principal components analysis kpca and kernel linear discriminant analysis klda considering the classification information to this end we combine the conventional kernel functions using genetic algorithm and genetic programming in linear and non linear forms respectively we use the classification error and the mutual information between features and classes in the kernel feature space as evolutionary fitness functions the proposed methods are evaluated on the basis of the university of california irvine uci datasets and aurora2 speech database we evaluate the methods using clustering validity indices and classification accuracy the experimental results demonstrate that kpca using a nonlinear combination of kernels based on genetic programming and the classification error fitness function outperforms conventional kpca using gaussian kernel and also kpca using linear combination of kernels

kernel class wise locality preserving projection in the recent years the pattern recognition community paid more attention to a new kind of feature extraction method the manifold learning methods which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure among them locality preserving projection lpp is one of the most promising feature extraction techniques however when lpp is applied to the classification tasks it shows some limitations such as the ignorance of the label information in this paper we propose a novel local structure based feature extraction method called class wise locality preserving projection clpp clpp utilizes class information to guide the procedure of feature extraction in clpp the local structure of the original data is constructed according to a certain kind of similarity between data points which takes special consideration of both the local information and the class information the kernelized nonlinear counterpart of this linear feature extractor is also established in the paper moreover a kernel version of clpp namely kernel clpp kclpp is developed through applying the kernel trick to clpp to increase its performance on nonlinear feature extraction experiments on orl face database and yale face database are performed to test and evaluate the proposed algorithm

stability and stabilizability of fuzzy neural linear control systems this paper discusses stability analysis of fuzzy neural linear fnl control systems which consist of combinations of fuzzy models neural network nn models and linear models the authors consider a relation among the dynamics of nn models those of fuzzy models and those of linear models it is pointed out that the dynamics of linear models and nn models can be perfectly represented by takagi sugeno t s fuzzy models whose consequent parts are described by linear equations in particular the authors present a procedure for representing the dynamics of nn models via t s fuzzy models next the authors recall stability conditions for ensuring stability of fuzzy control systems in the sense of lyapunov the stability criteria is reduced to the problem of finding a common lyapunov function for a set of lyapunov inequalities the stability conditions are employed to analyze stability of fnl control systems finally stability analysis for four types of fnl control systems is demonstrated

local analysis of continuous time takagi sugeno fuzzy system with disturbances bounded by magnitude or energy a lagrange multiplier method abstract this paper is concerned with the problem of computing the bounds of disturbances for continuous time takagi sugeno fuzzy model two kinds of disturbances are investigated by applying the non parallel distributed compensation non pdc law non quadratic lyapunov function and lagrange multiplier method one is bounded by magnitude the other is bounded by energy if the exogenous disturbances are bounded by magnitude the maximal value of the magnitude under which the closed loop system can be controlled is obtained by solving an optimization problem on the other hand if the disturbances are bounded by energy except for the maximal bound some conditions are derived to get two local regions a smaller and a bigger such that all the trajectories starting from the smaller region will remain in the bigger one which is required to be contained in a specific compact set where the original system can be represented as t s model exactly in the end the effectiveness of the proposed results is demonstrated by two examples

output feedback control of markov jump linear systems in continuous time this paper addresses the dynamic output feedback control problem of continuous time markovian jump linear systems the fundamental point in the analysis is an lmi characterization comprising all dynamical compensators that stabilize the closed loop system in the mean square sense the h sub 2 and h sub spl infin norm control problems are studied and the h sub 2 and h sub spl infin filtering problems are solved as a by product

multiple kernel learning algorithms in recent years several methods have been proposed to combine multiple kernels instead of using a single one these different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources different representations or different feature subsets in trying to organize and highlight the similarities and differences between them we give a taxonomy of and review several multiple kernel learning algorithms we perform experiments on real data sets for better illustration and comparison of existing algorithms we see that though there may not be large differences in terms of accuracy there is difference between them in complexity as given by the number of stored support vectors the sparsity of the solution as given by the number of used kernels and training time complexity we see that overall using multiple kernels instead of a single one is useful and believe that combining kernels in a nonlinear or data dependent way seems more promising than linear combination in fusing information provided by simple linear kernels whereas linear methods are more reasonable when combining complex gaussian kernels