convex conditions for robust stability analysis and stabilization of linear aperiodic impulsive and sampled data systems under dwell time constraints stability analysis and control of linear impulsive systems is addressed in a hybrid framework through the use of continuous time time varying discontinuous lyapunov functions necessary and sufficient conditions for stability of impulsive systems with periodic impulses are first provided in order to set up the main ideas extensions to the stability of aperiodic systems under minimum maximum and ranged dwell times are then derived by exploiting further the particular structure of the stability conditions the results are non conservatively extended to quadratic stability analysis of linear uncertain impulsive systems these stability criteria are in turn losslessly extended to stabilization using a particular yet broad enough class of state feedback controllers providing then a convex solution to the open problem of robust dwell time stabilization of impulsive systems using hybrid stability criteria relying finally on the representability of sampled data systems as impulsive systems the problems of robust stability analysis and robust stabilization of periodic and aperiodic uncertain sampled data systems are straightforwardly solved using the same ideas several examples are discussed in order to show the effectiveness and reduced complexity of the proposed approach

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

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

data driven linguistic modeling using relational fuzzy rules this paper presents a new approach to fuzzy rule based modeling of nonlinear systems from numerical data the novelty of the approach lies in the way of input partitioning and in the syntax of the rules this paper introduces interpretable relational antecedents that incorporate local linear interactions between the input variables into the inference process this modification improves the approximation quality and allows for limiting the number of rules additionally the resulting linguistic description better captures the system characteristics by exposing the interactions between the input variables

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

relational partitioning of fuzzy rules abstract we discuss the process of fuzzy systems model building and describe the role played by the partitioning of the input space we also introduce and investigate the idea of using fuzzy relations in the antecedent parts of the rules and show how to reason with these types of rules we discuss the ability of relational antecedent type rules to provide alternative partitioning of the input space their capacity to reduce the number of rules in the knowledge base and their role in a natural description of input output relations finally we look at the role these types of rules play in the typical fuzzy logic controller

existence of the semilocal chern simons vortices we consider the bogomol nyi equations of the abelian chern simons higgs model with su n global u 1 local symmetry this is a generalization of the well known abelian chern simons higgs model with u 1 local symmetry we prove existence of both topological and nontopological multivortex solutions of the system on the plane

the existence of chern simons vortices a new type of vortices called chern simons vortices is considered and the existence is established

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

on identification in fuzzy systems and its applications in control problems abstract the problem of identification in fuzzy systems described by the use of fuzzy equation is considered the identification method and its performance index is also presented the formal procedure of the identification algorithm is illustrated by means of a numerical example the possibility of using the proposed algorithm for the solution of a control problem is given as well

ultimate boundedness control for uncertain discrete time systems via set induced lyapunov functions in this note linear discrete time systems affected by both parameter and input uncertainties are considered the problem of the synthesis of a feedback control assuring that the system state is ultimately bounded within a given compact set containing the origin with an assigned rate of convergence is investigated it is shown that the problem has a solution if and only if there exists a certain lyapunov function which does not belong to a preassigned class of functions e g the quadratic ones but it is determined by the target set in which ultimate boundedness is desired one of the advantages of this approach is that we may handle systems with control constraints no matching assumptions are made for systems with linearly constrained uncertainties it is shown that such a function may be derived by numerically efficient algorithms involving polyhedral sets the resulting compensator may be implemented as a linear variable structure control

stability and l2 gain analysis for impulsive delay systems an impulse time dependent discretized lyapunov functional method abstract the problems of exponential stability and l 2 gain for a class of time delay systems with impulsive effects are studied the main tool used is the construction of an impulse time dependent complete lyapunov functional by dividing the impulse interval and delay interval into several segments the matrix functions of this functional are chosen to be continuous piecewise linear moreover an impulse time dependent weighting factor is introduced to coordinate the dynamical behavior of the nondelayed and integral terms of this functional along the trajectories of the system by applying this functional delay dependent sufficient conditions for exponential stability and l 2 gain are derived in terms of linear matrix inequalities as by products new delay independent sufficient conditions for the same problems are also derived the efficiency of the proposed results is illustrated by numerical examples

nontopological n vortex condensates for the self dual chern simons theory we prove the existence of nontopological n vortex solutions for an arbitrary number n of vortex points for the self dual chern simons higgs theory with t hooft periodic boundary conditions we use a shadowing type lemma to glue together any number of single vortices obtained as a perturbation of a radially symmetric entire solution of the liouville equation 2003 wiley periodicals inc

a transformed input domain approach to fuzzy modeling this paper presents an explanation of a fuzzy model considering the correlation among components of input data generally fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model but hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently which results in an ineffective partition of the input space in order to solve this problem this paper proposes a new fuzzy modeling algorithm which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data as a way to use the correlation and divide the input space the method of principal component is used finally the results of the computer simulation are given to demonstrate the validity of this algorithm

fuzzy sets as a basis for a theory of possibility the theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable more specifically if f is a fuzzy subset of a universe of discourse u u which is characterized by its membership function f then a proposition of the form x is f where x is a variable taking values in u induces a possibility distribution tx which equates the possibility of x taking the value u to f u the compatibility of u with f in this way x becomes a fuzzy variable which is associated with the possibility distribution tx in much the same way as a random variable is associated with a probability distribution in general a variable may be associated both with a possibility distribution and a probability distribution with the weak connection between the two expressed as the possibility probability consistency principle a thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is in the main possibilistic rather than probabilistic in nature thus by employing the concept of a possibility distribution a proposition p in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p several types of conditional translation rules are discussed and in particular a translation rule for propositions of the form x is f is possible where is a number in the interval 0 1 is formulated and illustrated by examples

brief paper stability properties of reset systems stability properties for a class of reset systems such as systems containing a clegg integrator are investigated we present lyapunov based results for verifying l 2 and exponential stability of reset systems our results generalize the available results in the literature and can be easily modified to cover l p stability for arbitrary email protected 1 several examples illustrate that introducing resets in a linear system may reduce the l 2 gain if the reset controller parameters are carefully tuned

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

criteria of asymptotic stability of differential and difference inclusions encountered in control theory abstract for a class of differential inclusions to which many of the practically important control systems can be reduced necessary and sufficient conditions for asymptotic stability of the zero solution are established by the method of lyapunov functions similar results are obtained for the corresponding class of difference inclusions

an online self constructing neural fuzzy inference network and its applications a self constructing neural fuzzy inference network sonfin with online learning ability is proposed in this paper the sonfin is inherently a modified takagi sugeno kang tsk type fuzzy rule based model possessing neural network learning ability there are no rules initially in the sonfin they are created and adapted as online learning proceeds via simultaneous structure and parameter identification in the structure identification of the precondition part the input space is partitioned in a flexible way according to an aligned clustering based algorithm as to the structure identification of the consequent part only a singleton value selected by a clustering method is assigned to each rule initially afterwards some additional significant terms selected via a projection based correlation measure for each rule will be added to the consequent part incrementally as learning proceeds the combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network a main feature of the sonfin in the parameter identification the consequent parameters are tuned optimally by either least mean squares or recursive least squares algorithms and the precondition parameters are tuned by a backpropagation algorithm to enhance the knowledge representation ability of the sonfin a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved

nonquadratic lyapunov functions for robust control abstract this paper deals with the problem of controlling a linear continuous time system with structured time varying parameter uncertainties and input disturbances with a lyapunov function approach in contrast with most of the previous results in the literature we do not confine our attention to the class of quadratic lyapunov functions conversely the basic motivation of this paper is to determine whether there exist other functions that can be conveniently used as candidate lyapunov functions this question has a positive answer the proposed class is that of polyhedral norms or more generally of polyhedral minkowski functionals we show that the class of these functions is universal in the sense that if the problem of ultimately bounding the state in an assigned convex set via state feedback control can be solved via a lyapunov function and a continuous state feedback compensator then it can be solved via a polyhedral lyapunov function and a possibly different continuous control moreover we show that the control can be piecewise linear a numerical technique for constructing the controller is presented for the case in which the uncertainty constraint sets are polyhedral

the existence of non topological multivortex solutions in the relativistic self dual chern simons theory we construct a general type of multivortex solutions of the self duality equations the bogomol nyi equations of 2 1 dimensional relativistic chern simons model with the non topological boundary condition near infinity for such construction we use a perturbation argument around the explicit solutions of the liouville equation

building sugeno type models using fuzzy discretization and orthogonal parameter estimation techniques this paper develops a new approach to building sugeno type models the essential idea is to separate the premise identification from the consequence identification while these are mutually related in the previous methods a fuzzy discretization technique is suggested to determine the premise of the model and an orthogonal estimator is provided to identify the consequence of the model the orthogonal estimator can provide information about the model structure or which terms to include in the model and final parameter estimates in a very simple and efficient manner the well known gas furnace data of box and jenkins is used to illustrate the proposed modeling approach and to compare its performance with other statistical and fuzzy modeling approaches it shows that the performance of the new approach compares favorably with these existing techniques

smooth transition autoregressive models and fuzzy rule based systems functional equivalence and consequences in this work we will explore the theoretical connections existing between fuzzy rule based systems frbs applied on univariate time series and two statistical reference tools the autoregressive ar models and the smooth transition autoregressive star model we will show that a tsk fuzzy rule happens to be a localised ar model and that a star model can hence be interpreted as a restricted frbs several consequences derive from this fact and we will explore some of them including a statistical inference based procedure to incrementally build frbs or the linguistic interpretation of star models finally an econometric example is given to illustrate the core idea

fuzzy classification using pattern discovery rule based classifiers allow rationalization of classifications made this in turn improves understanding which is essential for effective decision support as a rule based classifier the pattern discovery pd algorithm functions well in discrete nominal and continuous data domains a drawback when using pd as a classifier for decision support is that it has an unbounded decision space that confounds the understanding of the degree of support for a decision incorporating pd into a fuzzy inference system fis allows the the degree of support for a decision to be expressed with intuitively understandable terms in addition using discrete algorithms in continuous domains can result in reduced accuracy due to quantization fuzzification reduces this ldquocost of quantizationrdquo and improves classification performance in this work the pd algorithm was used as a source of rules for a series of fiss implemented using different rule weighting and defuzzification schemes each providing a linguistic basis for rule description and a bounded space for expression of decision support the output of each fis consists of a suggested outcome a strong confidence metric describing suggestions within this space and a linguistic expression of the rules this constitutes a stronger basis for decision making than that provided by pd alone a variety of synthetic continuous class distributions with varying degrees of separation was used to evaluate the performance of fuzzy pd back propagation and bayesian classifiers overall the accuracy of the fuzzy system was found to be similar but slightly below that of the inherently continuous valued classifiers and was somewhat improved with respect to the pd classifiers for the difficult spiral class distributions studied the fuzzy classifiers were able to make more classifications than the pd classifiers the correct classification rates for the fuzzy classifiers were similar across the various rule weighting and defuzzification schemes demonstrating the strength of the statistical method for rule generation analysis of several real world data sets shows that a pd based fis has comparable performance to a neuro fuzzy system the use of a pd based fis however provides insight into the structure of the data analyzed not available through the other approaches

an identification algorithm in fuzzy relational systems abstract the paper presents an effective identification method in fuzzy relational systems we propose an algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques the usefulness of the method provided is demonstrated by means of two numerical examples also a possible way of generating a linguistic decision making algorithm is discussed

on the analysis of fuzzy logic controllers abstract in this paper we address the issue of analyzing the properties of the fuzzy logic controller flc we introduce two basic models of the flc the first representation uses the concept of the quasilinear fuzzy model the flc is approximated by a virtual pid controller consisting of linear controller connected in parallel the second interpretation of the flc is from the point of view of nonlinear control the flc is analyzed as a sliding mode like controller based on these two alternative representations are derived rules for designing and tuning fuzzy logic controllers

a new approach to fuzzy modeling this paper proposes a new approach to fuzzy modeling the suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as takagi and sugeno s model 1985 because it has the same structure as that of takagi and sugeno s model it is also as easy to implement as sugeno and yasukawa s model 1993 because its identification mimics the simple identification procedure of sugeno and yasukawa s model the suggested algorithm is composed of two steps coarse tuning and fine tuning in coarse tuning fuzzy c regression model fcrm clustering is used which is a modified version of fuzzy c means fcm in fine tuning gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models finally some examples are given to demonstrate the validity of this algorithm

neuro fuzzy rule generation survey in soft computing framework the present article is a novel attempt in providing an exhaustive survey of neuro fuzzy rule generation algorithms rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network fuzzy sets are an aid in providing this information in a more human comprehensible or natural form and can handle uncertainties at various levels the neuro fuzzy approach symbiotically combining the merits of connectionist and fuzzy approaches constitutes a key component of soft computing at this stage to date there has been no detailed and integrated categorization of the various neuro fuzzy models used for rule generation we propose to bring these together under a unified soft computing framework moreover we include both rule extraction and rule refinement in the broader perspective of rule generation rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint models are grouped on the basis of their level of neuro fuzzy synthesis use of other soft computing tools like genetic algorithms and rough sets are emphasized rule generation from fuzzy knowledge based networks which initially encode some crude domain knowledge are found to result in more refined rules finally real life application to medical diagnosis is provided

constructive necessary and sufficient condition for the stability of quasi periodic linear impulsive systems the technical note provides a computation oriented necessary and sufficient condition for the global exponential stability of linear impulsive systems whose impulsions are assumed to occur quasi periodically based on the set theoretic conditions for robust stability of uncertain linear systems the existence of polyhedral lyapunov functions is proved to be necessary and sufficient for global exponential stability of quasi periodic linear impulsive systems a constructive method is developed for testing the stability of the system and for computing set induced polyhedral lyapunov functions the method leads to an algorithm whose complexity is similar to the standard algorithm related to discrete time parametric uncertain systems with the state matrix belonging to a convex polytopic set

survey paper set invariance in control the properties of positively invariant sets are involved in many different problems in control theory such as constrained control robustness analysis synthesis and optimization in this paper we provide an overview of the literature concerning positively invariant sets and their application to the analysis and synthesis of control systems

lyapunov conditions for input to state stability of impulsive systems this paper introduces appropriate concepts of input to state stability iss and integral iss for impulsive systems i e dynamical systems that evolve according to ordinary differential equations most of the time but occasionally exhibit discontinuities or impulses we provide a set of lyapunov based sufficient conditions for establishing these iss properties when the continuous dynamics are iss but the discrete dynamics that govern the impulses are not the impulses should not occur too frequently which is formalized in terms of an average dwell time adt condition conversely when the impulse dynamics are iss but the continuous dynamics are not there must not be overly long intervals between impulses which is formalized in terms of a novel reverse adt condition we also investigate the cases where i both the continuous and discrete dynamics are iss and ii one of these is iss and the other only marginally stable for the zero input while sharing a common lyapunov function in the former case we obtain a stronger notion of iss for which a necessary and sufficient lyapunov characterization is available the use of the tools developed herein is illustrated through examples from a micro electro mechanical system mems oscillator and a problem of remote estimation over a communication network

fundamental properties of reset control systems reset controllers are linear controllers that reset some of their states to zero when their input is zero we are interested in their feedback connection with linear plants and in this paper we establish fundamental closed loop properties including stability and asymptotic tracking this paper considers more general reset structures than previously considered allowing for higher order controllers and partial state resetting it gives a testable necessary and sufficient condition for quadratic stability and links it to both uniform bounded input bounded state stability and steady state performance unlike previous related research which includes the study of impulsive differential equations our stability results require no assumptions on the evolution of reset times