a new algorithm for testing diagnosability of fuzzy discrete event systems failure diagnosis and detection of fuzzy discrete event systems play a significant role in the study of complex systems in this paper we investigate the diagnosability of fuzzy discrete event systems by proposing a new algorithm based on the concept of undistinguishable strings moreover a necessary and sufficient condition for fuzzy diagnosability is obtained in terms of certain properties of the diagnoser which is constructed with respect to the minimal observable event the computing process to check the diagnosability of fuzzy dess and some examples serving to illuminate the applications are developed and described

fault detection and isolation based on fuzzy automata fuzzy automata are proposed for fault diagnosis the output of the monitored system is partitioned into linear segments which in turn are assigned to pattern classes templates with the use of membership functions a sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system if the automata reach their final states i e the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold then normal operation is deduced otherwise a failure is diagnosed fault diagnosis of a dc motor and detection of abnormalities in the ecg signal are used as case studies

design of decentralized critical observers for networks of finite state machines a formal method approach abstract motivated by safety critical applications in cyber physical systems in this paper we study the notion of critical observability and design of observers for networks of finite state machines fsms critical observability corresponds to the possibility of detecting if the current state of an fsm is in a given region of interest called set of critical states a critical observer detects on line the occurrence of critical states when a large scale network of fsms is considered the construction of such an observer is prohibitive because of the large computational effort needed we propose a decentralized architecture for critical observers of networks of fsms where on line detection of critical states is performed by local critical observers each associated with an fsm of the network which do not need to interact for the efficient design of decentralized critical observers we first extend on the fly algorithms traditionally used in the community of formal methods for the verification and control design of fsms we then extend to networks of fsms bisimulation theory traditionally given in the community of formal methods for single fsms the proposed techniques provide a remarkable computational complexity reduction as discussed throughout the paper and also demonstrated by means of illustrative examples

min transitivity of fuzzy leftness relationship and its application to decision making abstract we show that the fuzzy leftness relationship left a b between two intervals a and b on a line which we had recently defined for modeling spatial relationships satisfies the min transitivity property the previous definitions of leftness given by krishnapuram et al and miyajima et al do not have this property the min transitivity property plays an important role in fuzzy decision making based on orlovsky s result we give several general schemes for constructing concrete min transitive relations based on our left a b for use in fuzzy decision making

distributed trace estimation under timing mismatch and channel distortion in conservative or complete trace based distributed fault diagnosis it is common to compute for each component a preliminary local estimate consisting of all possible traces that can generate the same local symptom i e a collection of sequences of observable events as the one being received from that component up to a specific time instant then communication among local trace estimators can be used to refine those preliminary local estimates an approach has been proposed by su and wonham to perform such refinement in terms of achieving global or local consistency among local estimates but that approach does not consider timing mismatch and channel distortion in the process of collecting local symptoms which may result in incorrect estimates in practical applications in this paper we will provide a new approach to handle trace estimation under timing mismatch and channel distortion

on comparing interval numbers abstract this paper first presents a brief survey of the existing works on comparing and ranking any two interval numbers on the real line and then on the basis of this gives two approaches to compare any two interval numbers the first one describes a value judgement index along with a discussion on its strength and weakness over the other approaches this index has also been called an optimistic decision maker s preference index the other approach defines strict and fuzzy preference ordering between any two interval numbers from a pessimistic decision maker s point of view choice of different decision makers with different degrees of pessimism has also been considered

diagnosability of fuzzy discrete event systems a fuzzy approach in order to more effectively cope with the real world problems of vagueness fuzzy discrete event systems fdess were proposed by lin and ying recently then we and cao and ying investigated the supervisory control of fdess independently in this paper we are concerned with another important issue of fdess the failure diagnosis more specifically 1 we propose a ldquofuzzy diagnosabilityrdquo approach by introducing a fuzzy diagnosability function to characterize the diagnosability degree which takes values in the interval 0 1 rather than 0 1 2 based on the observability of events we formalize the construction of the diagnosers that are used to perform fuzzy diagnosis 3 a number of basic properties of the diagnosers are investigated in particular we present a necessary and sufficient condition for failure diagnosis of fdess our results generalize the important consequences of the diagnosability for crisp discrete event systems dess introduced by sampath et al the newly proposed approach allows us to deal with the problem of diagnosability for both crisp dess and fdess 4 in addition a method for checking the fuzzy diagnosability for fdess is proposed also some examples are provided to illustrate the application of the diagnosability of fdess

a theory of diagnosis from first principles suppose one is given a description of a system together with an observation of the system s behaviour which conflicts with the way the system is meant to behave the diagnostic problem is to determine those components of the system which when assumed to be functioning abnormally will explain the discrepancy between the observed and correct system behaviour we propose a general theory for this problem the theory requires only that the system be described in a suitable logic moreover there are many such suitable logics e g first order temporal dynamic etc as a result the theory accommodates diagnostic reasoning in a wide variety of practical settings including digital and analogue circuits medicine and database updates the theory leads to an algorithm for computing all diagnoses and to various results concerning principles of measurement for discriminating among competing diagnoses finally the theory reveals close connections between diagnostic reasoning and nonmonotonic reasoning

on effective computation of supremal local supports in distributed diagnosis it may be useful to achieve local consistency among local estimates for that purpose the computational procedure for local consistency cplc was proposed to achieve the supremal local support which represents one type of local consistency it has been shown that if cplc terminates then the result is in fact the supremal local support however in this paper it is shown that even if all initial estimates are regular languages the termination of cplc is undecidable moreover these difficulties are not confined to this specific procedure it is undecidable whether the supremal local support corresponding to an arbitrary collection of regular initial languages is componentwise empty consequently the supremal local support is effectively uncomputable on the other hand a sufficient condition is given which guarantees that cplc terminates and that the supremal local support can be computed in time linear in the number of component diagnosers

acceptable consistency analysis of interval reciprocal comparison matrices when a decision maker expresses his her opinions by means of an interval reciprocal comparison matrix the study of consistency becomes a very important aspect in decision making in order to avoid a misleading solution in the present paper an acceptably consistent interval reciprocal comparison matrix is defined which can be reduced to an acceptably consistent crisp reciprocal comparison matrix when the intervals become exact numbers an interval reciprocal comparison matrix with unacceptable consistency can be easily adjusted such that the revised matrix possesses acceptable consistency utilizing a convex combination method a family of crisp reciprocal comparison matrices with acceptable consistency can be obtained whose weights are further found to exhibit a style of convex combination and aggregated to obtain interval weights from an acceptably consistent interval reciprocal comparison matrix a novel simple yet effective formula of possibility degree is presented to rank interval weights numerical results are calculated to show the quality and quantity of the proposed approaches and compare with other existing procedures

managing non homogeneous information in group decision making within the frame of decision aid literature decision making problems with multiple sources of information have drawn the attention of researchers from a wide spectrum of disciplines in decision situations with multiple individuals each one has his own knowledge on the alternatives of the decision problem the use of information assessed in different domains is not a seldom situation this non homogeneous information can be represented as values belonging to domains with different nature as linguistic numerical and interval valued or can be values assessed in label sets with different granularity multi granular linguistic information decision processes for solving these problems are composed by two phases aggregation and exploitation the main problem to deal with non homogeneous contexts is how to aggregate the information assessed in these contexts in this paper taking as base the 2 tuple fuzzy linguistic representation model we shall develop an aggregation process for dealing with non homogeneous contexts in first place we shall develop an aggregation process for combining numerical interval valued and linguistic information afterwards we shall propose different extensions of this process to deal with contexts in which can appear other type of information as intuitionistic fuzzy sets or multi granular linguistic information

arc and path consistence revisited mackworth and freuder have analyzed the time complexity of several constraint satisfaction algorithms 5 we present here new algorithms for arc and path consistency and show that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms a refined solution for the path consistency problem is proposed however the space complexity of the path consistency algorithm makes it practicable only for small problems these algorithms are the result of the synthesis techniques used in image a general constraint satisfaction system and local consistency methods 3

brief paper abstraction based verification of codiagnosability for discrete event systems in this paper we investigate the verification of codiagnosability for discrete event systems des that is it is desired to ascertain if the occurrence of system faults can be detected based on the information of multiple local sites that partially observe the overall des as an improvement of existing codiagnosability tests that resort to the original des with a potentially computationally infeasible state space we propose a method that employs an abstracted system model on a smaller state space for the codiagnosability verification furthermore we show that this abstraction can be computed without explicitly evaluating the state space of the original model in the practical case where the des is composed of multiple subsystems

a least deviation method to obtain a priority vector of a fuzzy preference relation based on the transfer relationship between fuzzy preference relation and multiplicative preference relation this paper proposes a least deviation method to obtain a priority vector of a fuzzy preference relation and studies its desirable properties a simple convergent iterative algorithm is given and its practicality is shown by two numerical examples

dominance degrees for intervals and their application in multiple attribute decision making abstract since intervals are used to express decision information in various decision making problems it is necessary to give a feasible method to rank them the most commonly used method is to employ the possibility degree to compare intervals in pairs then construct a fuzzy preference relation generally the constructed fuzzy preference relation does not keep additive multiplicative consistency in order to ensure the consistency of decision information optimization models are constructed by using the logical relationship between the elements of the consistent fuzzy preference relation and their weights then by solving the optimization models consistent fuzzy preference relations are obtained the consistent fuzzy preference relations are the proxy of the original inconsistent fuzzy preference relation constructed by the possibility degree however such treatment is flawed the inconsistent fuzzy preference relation generated from the possibility degree can t guarantee the reliability of the result while the proxy consistent fuzzy preference relation would cause bias to solve this problem the additive dominance degree and multiplicative dominance degree for intervals are proposed as a result of indirect comparison the fuzzy preference relations are constructed from the dominance degrees of intervals to ensure the consistency of decision information in the process of ranking intervals this can avoid decision making bias furthermore a new approach to interval multiple attribute decision making is proposed based on the dominance degrees

uncertainty and rank order in the analytic hierarchy process abstract the analytic hierarchy process uses paired comparisons to derive a scale of relative importance for alternatives we investigate the effect of uncertainty in judgment on the stability of the rank order of alternatives the uncertainty experienced by decision makers in making comparisons is measured by associating with each judgment an interval of numerical values the approach leads to estimating the probability that an alternative or project exchanges rank with other projects these probabilities are then used to calculate the probability that project would change rank at all we combine the priority of importance of each project with the probability that it does not change rank to obtain the final ranking

asymptotic tracking control of uncertain nonlinear systems with unknown actuator nonlinearity and unknown gain signs two adaptive control schemes for a class of uncertain nonlinear systems preceded by actuator nonlinearity are presented we consider two possible input nonlinearities that is backlash like hysteresis and symmetric dead zone which can be simultaneously dealt with by using the proposed compensation controllers the knowledge on the signs of the unknown plant parameters and the parameters in the input nonlinearity models is not required only the boundedness of the reference signal is assumed the nussbaum gain approach is employed to remove the assumption on the control gain sign the first scheme is based on the backstepping technique the final step of the recursive design is crucial for the closed loop stability in the second scheme a direct adaptive control law is designed the controller has a simple structure and only one adaptation parameter is updated in both controllers it is shown that asymptotic tracking is ensured two numerical examples are provided to show the effectiveness of the theor

interval weight generation approaches based on consistency test and interval comparison matrices a simple yet pragmatic method of consistency test is developed to check whether an interval comparison matrix is consistent or not linear programming method is used to derive consistent interval weights from consistent interval comparison matrices and to aggregate local interval weights to generate global interval weights in cases where an interval comparison matrix is inconsistent an eigenvector method based nonlinear programming nlp approach is developed to generate interval weights that can meet pre determined consistency requirements a simple and effective preference ranking method is utilized to compare the interval weights of criteria or rank alternatives three numerical examples including a hierarchical ahp decision problem are provided to illustrate the validity and practicality of the proposed methods

numerical methods for interval and fuzzy number comparison based on the probabilistic approach and dempster shafer theory there are many different methods for interval and fuzzy number comparison proposed in the literature which provide the results of comparison in the form of a real or boolean value in this paper we use the dempster shafer theory of evidence with its probabilistic interpretation to justify and construct the method which provides the result of comparison in the form of an interval or fuzzy number the complete and consistent set of expressions for inequality and equality relations between intervals and fuzzy numbers has been obtained in the framework of the probabilistic approach these relations make it possible to compare intervals and fuzzy numbers with real values and may be considered as an asymptotic limit of the results we obtain using the dempster shafer theory a natural fuzzy extension of the proposed approach is considered and discussed using some illustrative examples

decentralized diagnosis of stochastic discrete event systems we investigate the decentralized diagnosis of stochastic discrete event systems sdess by using multiple local stochastic diagnosers each possessing its own sensors to deal with different information we formalize the notions of decentralized diagnosis for sdess by defining the concept of codiagnosability for stochastic automata in which any communication among the local stochastic diagnosers or to any coordinators is not involved these notions are weaker than the corresponding notions of decentralized diagnosis of classical dess a stochastic system being codiagnosable means that a fault can be detected by at least one local stochastic diagnoser within a finite delay we construct a codiagnoser from a given stochastic system with a finite number of projections whose each diagnosis component uses the complete model of the system we also deal with a number of basic properties of the codiagnoser in particular a necessary and sufficient condition of the codiagnosability for sdess is presented which generalizes the corresponding results of centralized diagnosis for sdess also we give a computing method in detail to check the codiagnosability of sdess as an application of our results some examples are described

a distance based framework to deal with ordinal and additive inconsistencies for fuzzy reciprocal preference relations consistency of preference relations is related to rationality the lack of consistency in decision making often leads to misleading solutions this paper studies the ordinal and cardinal consistency problems of fuzzy reciprocal preference relations first the concept of ordinal consistency of fuzzy reciprocal preference relations is introduced and analyzed a lp distance based method is proposed to formulate the underlying optimization problems as goal programming gp models for ordinal and additive consistency problems respectively by setting different p three gp models are obtained a a linear gp model where p 1 b a minmax gp model where p and c an extended gp model which integrates the two previous models in particular cases utilizing these models we can solve the ordinal and additive consistency problems for fuzzy reciprocal preference relations the proposed model can preserve the initial preference information as much as possible finally a numerical example and comparative analysis are provided to show effectiveness and validity 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

interval priorities in ahp by interval regression analysis abstract this paper proposes an interval approach for obtaining interval weights of priorities in the analytic hierarchy process ahp reflecting inconsistency of pairwise comparison ratios given by a decision maker even if pairwise comparison values with respect to preference are given as crisp values the priority weights should be estimated as intervals because of a decision maker s uncertainty of judgments we deal with two types of comparison matrices represented as crisp and interval elements this allows some uncertainty of estimations made by a decision maker the proposed approach can obtain the estimated interval priorities since interval regression analysis is based on the concept of possibility the interval weights obtained by the proposed method can be interpreted as the possible ranges estimated from the given data the sum of widths of the obtained intervals can be regarded as the degree of inconsistency included in the given data it is emphasized that interval data of preference can obtain the lower and upper models for the estimated weight intervals

a two stage logarithmic goal programming method for generating weights from interval comparison matrices a two stage logarithmic goal programming tlgp method is proposed to generate weights from interval comparison matrices which can be either consistent or inconsistent the first stage is devised to minimize the inconsistency of interval comparison matrices and the second stage is developed to generate priorities under the condition of minimal inconsistency the weights are assumed to be multiplicative rather than additive in the case of hierarchical structures a nonlinear programming method is used to aggregate local interval weights into global interval weights a simple yet practical preference ranking method is investigated to compare the interval weights of criteria or rank alternatives in a multiplicative aggregation process the proposed tlgp is also applicable to fuzzy comparison matrices when they are transformed into interval comparison matrices using a level sets and the extension principle six numerical examples including a group decision analysis problem with a group of comparison matrices a hierarchical decision problem and a fuzzy decision problem using fuzzy comparison matrix are examined to show the applications of the proposed methods comparisons with other existing procedures are made whenever possible

analysis and synthesis of nonlinear time delay systems via fuzzy control approach takagi sugeno ts fuzzy models 1985 1992 can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input output i o submodels in this paper the ts fuzzy model approach is extended to the stability analysis and control design for both continuous and discrete time nonlinear systems with time delay the ts fuzzy models with time delay are presented and the stability conditions are derived using lyapunov krasovskii approach we also present a stabilization approach for nonlinear time delay systems through fuzzy state feedback and fuzzy observer based controller sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain are derived through the numerical solution of a set of coupled linear matrix inequalities an illustrative example based on the cstr model is given to design a fuzzy controller

safe diagnosability of stochastic discrete event systems recently safe diagnosability of discrete event systems dess was investigated by paoli and lafortune which was viewed as the first necessary step of fault tolerant supervision in this paper we consider the problem of safe diagnosability in the framework of stochastic discrete event systems sdess we define the notion of safe diagnosability for stochastic automata in which fault detection occurs before any given forbidden string in the failed mode of system is executed the relationship between diagnosability and safe diagnosability for sdess is analyzed in particular a necessary and sufficient condition for safe diagnosability of sdess is presented by constructing the recognizer of illegal language and the safe diagnoser some examples are described to illustrate the results

compiling constraint satisfaction problems reference lia article 1999 003doi 10 1016 s0004 3702 99 00077 6view record in web of science record created on 2006 12 13 modified on 2017 05 12

a generalized framework for inference based diagnosis of discrete event systems capturing both disjunctive and conjunctive decision making we have previously introduced an inference based framework for decentralized decision making comprising of multiple observers each with its own partial observation where inferencing over the ambiguities of the self and the others is used to issue local decisions and a global decision is taken to be the one possessing the minimum ambiguity level in this setting we previously introduced the notion of n inference vee diagnosability to characterize the existence of a disjunctive decentralized diagnosis scheme so that any fault can be detected within a bounded delay using at most n levels of inferencing by one of the diagnosers while the disjunctive scheme relies on one of the diagnosers making the failure decision the dual conjunctive scheme relies on none of the diagnosers making the nonfailure decision it is known that the two schemes are incomparable and in this paper we extend our earlier work to provide a more general framework introducing the notion of n inference diagnosability capturing both disjunctive and conjunctive schemes we also develop a method for verifying n inference diagnosability as well as discuss several of its useful properties

nonconflict check by using sequential automaton abstractions based on weak observation equivalence in ramadge wonham supervisory control theory we often need to check nonconflict of plants and corresponding synthesized supervisors for a large system such a check imposes a great computational challenge because of the complexity incurred by the composition of plants and supervisors in this paper we present a novel procedure based on automaton abstractions which removes internal transitions of relevant automata at each step allowing the nonconflict check to be performed on relatively small automata even though the original product system can be fairly large

global and local consistencies in distributed fault diagnosis for discrete event systems in this paper we present a unified framework for distributed diagnosis we first introduce the concepts of global and local consistency in terms of supremal global and local supports then present two distributed diagnosis problems based on them after that we provide algorithms to achieve supremal global and local supports respectively and discuss in detail the advantages and disadvantages of each finally we present an industrial example to demonstrate our distributed diagnosis approach

fault diagnosis in discrete event systems framework and model reduction a state based approach for online passive fault diagnosis in systems modeled as finite state automata is presented in this framework the system and the diagnoser the fault detection system do not have to be initialized at the same time furthermore no information about the state or even the condition failure status of the system before the initiation of diagnosis is required the design of the fault detection system in the worst case has exponential complexity a model reduction scheme with polynomial time complexity is introduced to reduce the computational complexity of the design diagnosability of failures is studied and necessary and sufficient conditions for failure diagnosability are derived