Showing papers on "Fuzzy associative matrix published in 1991"
TL;DR: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.
Abstract: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
1,476 citations
Book•
01 Jan 1991TL;DR: When you read more every page of this fuzzy systems theory and its applications, what you will obtain is something great.
Abstract: Read more and get great! That's what the book enPDFd fuzzy systems theory and its applications will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this fuzzy systems theory and its applications, what you will obtain is something great.
603 citations
TL;DR: The hierarchical fuzzy control algorithm developed in this paper is applied to control the feedwater flow to a steam generator of a power plant and results show that the hierarchical fuzzy controller yields superior performance over the conventional PID controller.
Abstract: In a conventional rule based fuzzy control system, the rules are of the following form: if (condition) then (action), and all rules are essentially in a random order. The number of rules increases exponentially as the number of the system variables, on which the fuzzy rules are based, is increased. In this paper, the rules are structured in a hierarchical way so that the total number of rules will be a linear function of the system variables. The hierarchical fuzzy control algorithm developed in this paper is applied to control the feedwater flow to a steam generator of a power plant. The simulation results show that the hierarchical fuzzy controller yields superior performance over the conventional PID controller.
378 citations
TL;DR: The concepts of fuzzy continuity, product and quotient spaces are presented, and their fundamental properties are obtained in fuzzifying topology.
259 citations
TL;DR: An introduction to the theory of fuzzy binary relations and some related concepts is presented, including major classes of fuzzy orderings defined and classified with respect to the duality relation.
161 citations
13 Aug 1991
TL;DR: The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy and is applied to predicting a chaotic time series.
Abstract: A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series. >
154 citations
TL;DR: It is shown that the fuzzy quadratic equation, with real fuzzy number coefficients, always has a (new) solution and the previous solution based on the extension principle is a subset of the new solution.
146 citations
TL;DR: The concept of the quasilinear fuzzy model (QLFM) of a dynamic nonlinear system is introduced, and the problem of its identification, state-space, and transfer function representation is discussed.
138 citations
08 Jul 1991
TL;DR: A contribution to the theoretical development of fuzzy neural network theory is presented and a few methods of how these neurons change themselves during learning to improve their performance are given.
Abstract: A contribution to the theoretical development of fuzzy neural network theory is presented. Three types of fuzzy neuron models are proposed. Neuron I is described by logical equations of 'if-then' rules; its inputs are either fuzzy sets or crisp values. Neuron II, with numerical inputs, and neuron III, with fuzzy inputs, are considered to be simple extensions of non-fuzzy neurons. A few methods of how these neurons change themselves during learning to improve their performance are also given. The application of the non-fuzzy neural network approach to fuzzy information processing is briefly discussed. >
100 citations
TL;DR: A fuzzy logic control scheme to enhance the overall stability of a multi-machine power system and is easy to implement, and requires a low amount of computation because of its simple control rules, and required data.
01 Feb 1991
TL;DR: F fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture are discussed.
Abstract: Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
TL;DR: It is shown that the regularity of a fuzzy interval is preserved after applying an arithmetic operation with a nonzero real number and the formulas for calculating the defuzzified value of the arithmetic operations between a regular fuzzy interval and a real number are derived.
TL;DR: It is shown that the property of approximation is preserved when the maxmin compositional rule of inference is applied to approximately equal values of fuzzy variables and fuzzy relations.
TL;DR: Some important theorems of fuzzy numbers and the fuzzy continuous function on the M -closed interval are discussed.
TL;DR: A fuzzy integral of Riemann type is defined through atoms, and it is proved that a continuous function is fuzzy integrable in the sense ofRiemann and has the same integral as that of Sugeno.
TL;DR: It is shown that the fuzzy intersection of two fuzzy inner product spaces is also a fuzzy outer product space and some results on these are proved.
TL;DR: The Cartesian product of fuzzy inner product spaces are introduced and the fuzzy Schwarz inequality for fuzzy inner products is established, as well as investigating the relations between their underlying topologies.
24 Aug 1991
TL;DR: The basic methodology of fuzzy logic controllers was empirically developed in the late seventies and early eighties and has not changed much since, but recently a revival of rather theoretically-oriented studies has been observed in order to build a strong methodology for fuzzy Logic controllers.
Abstract: Fuzzy logic controllers have encountered an extraordinary success in a great variety of industrial applications in the last few years, especially in Japan. The principle of fuzzy controllers, first outlined by Zadeh[31] and then successfully experimented by Mamdani and Assilian[20], consists of synthesizing a control law for a system from fuzzy rules, usually provided by experts, which state the action(s) to do in typical situations, in contrast with the standard approach to automatic control which requires a model of the system to control. Each rule more or less applies to a fuzzy class of situations and an interpolation operation is performed between the conclusion parts of the selected rules, on the basis of the degrees of compatibility between the condition parts of these rules and the current situation encountered by the system. The reader is referred to Mamdani[19], Sugeno[24] for introductions and to Lee[16] and Berenji[5] for surveys. The basic methodology of fuzzy logic controllers was empirically developed in the late seventies and early eighties and has not changed much since. Recently, a revival of rather theoretically-oriented studies has been observed in order to build a strong methodology for fuzzy logic controllers. Thus the analytical comparison between a fuzzy controller and a proportional-integral controller[30], the limit behavior of fuzzy controllers[6], the stability of fuzzy controllers[27], [26], adaptive techniques for fuzzy controllers, e.g. [22]; [1]; [13], and the use of neural network methods for learning fuzzy rules and implementation issues[17] [28] have been discussed.
TL;DR: Using the concept of a fuzzy set, a fuzzy primary ideal is defined and some fundamental results concerning these notions are proved.
TL;DR: A decision theoretic view in designing an optimal fuzzy controller is taken based on the general purpose fuzzy expert system shell Flops to differentiate between decision making under certainty, risk, or uncertainty depending whether or not random elements are present in the system.
Book•
01 Dec 1991
TL;DR: This book discusses fuzzy set theory and modelling of natural language semantics, fuzzy optimization and mathematical programming, and interactive decision making for multiobjective linear programming problems with fuzzy parameters based on a solution concept incorporating fuzzy goals.
Abstract: 1 Introductory Sections- Fuzzy set theory and modelling of natural language semantics- A survey of fuzzy optimization and mathematical programming- 2 Fuzzy Optimization: General Issues and Related Topics- Minimizing a fuzzy function- A concept of optimality for fuzzified mathematical programming problems- Some properties of possibilistic linear equality systems with weakly noninteractive fuzzy numbers- Fuzzy preferences in linear programming- Implication relations, equivalence relations and hierarchical structure of attributes in multiple criteria decision making- Uncertain multiobjective programming as a game against nature- Approaching fuzzy integer linear programming problems- Interactive bicriteria integer programming: a performance analysis- Interactive approaches for solving some decision making problems in the Czechoslovak power industry- 3 Issues Related to Interactive Decision Making- Elicitation of opinions by means of possibilistic sequences of questions- Searching fuzzy concepts in a natural language data base- Reconfigurable network architecture for distributed problem solving- 4 Algorithms and Software for Interactive Fuzzy Optimization- Interactive decision making for multiobjective linear programming problems with fuzzy parameters based on a solution concept incorporating fuzzy goals- FULP - a PC-supported procedure for solving multicriteria linear programming problems with fuzzy data- 'FLIP': multiobjective fuzzy linear programming software with graphical facilities- FPLP - a package for fuzzy and parametric linear programming problems- An expert system for the solution of fuzzy linear programming problems
01 Jan 1991
TL;DR: The modeling method is shown to be useful for abstracting concepts by refining fuzzy causal relations as outline knowledge and it is shown how the fuzzy cognitive model is useful for the expression of subjective information.
Abstract: A useful fuzzy cognitive model using causal concepts is proposed to model the operations of a physical plant. In this model, the sequence of events is qualitatively represented by a fuzzy associative memory system that we call FAMOUS (Fuzzy Associative Memory Organizing Units System). The modeling method has three features: 1) fuzzy knowledge representation using causal relations, 2) refinement of the fuzzy causal relationships using unsupervised learning, and 3) causal inference with feedback associative memories. Using an example, the modeling method is shown to be useful for abstracting concepts by refining fuzzy causal relations as outline knowledge. We also show how the fuzzy cognitive model is useful for the expression of subjective information.
TL;DR: A concept of concave fuzzy set is introduced in this connection for fuzzy geometric shapes like point, line, circle, ellipse and polygon.
TL;DR: This is an overview paper presenting the main results obtained by the author and his colleagues in the field of fuzzy logic, namely that of prepositional and first-order fuzzy logic based on a residuated lattice of truth values.
Abstract: This is an overview paper presenting the main results obtained by the author and his colleagues in the field of fuzzy logic and modelling of natural language semantics and is composed of an introduction followed by two main parts. Section 2 discusses our results in terms of fuzzy logic, namely that of prepositional and first-order fuzzy logic based on a residuated lattice of truth values. Section 3 presents the concept of the Alternative mathematical Model of natural Language semantics and pragmatics (AML) the development of which is based on a philosophical approach rather than the approaches usually adopted in most classical papers.
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TL;DR: A fuzzy controller which accepts and produces fuzzy data (linguistic variables) as well as crisp data for the control of man-machine systems is described.
01 Jan 1991
TL;DR: It is pointed out that by merging the advantages of fuzzy expert systems and neural networks one can arrive at a more powerful yet more flexible system for inferencing and learning.
Abstract: It is pointed out that by merging the advantages of fuzzy expert systems and neural networks one can arrive at a more powerful yet more flexible system for inferencing and learning. The advantages of fuzzy expert systems are their ability to provide nonlinear mapping through the membership functions and fuzzy rules, and the ability to deal with fuzzy information and incomplete and/or imprecise data. The merger of these two concepts is explained using the truck backer-upper control problem. Novel network architectures obtained by merging these two concepts and simulation results for the truck backer-upper problem using the architecture are shown. >
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TL;DR: Some models of quantum mechanics which use fuzzy set approaches are presented: fuzzy measurable spaces and fuzzy quantum posets of two types.
Abstract: We present some models of quantum mechanics which use fuzzy set approaches: fuzzy measurable spaces and fuzzy quantum posets of two types. Also presented are compatibility problems, observables and their representations, functional calculus of observables, and states (= probability measures).
TL;DR: The purpose of the present paper is to introduce some further concepts, such as convex fuzzy sets etc., in fuzzy linear spaces over any valued field.