Showing papers on "Fuzzy associative matrix published in 1984"
TL;DR: An algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques is proposed.
524 citations
TL;DR: The Fuzzy Linear Mathematical Programming problem is easily solved because of the concept of fuzzy objective based on the Fuzzification Principle and the relationship of duality among fuzzy constraints and fuzzy objectives is given.
177 citations
01 Mar 1984
TL;DR: The concept of the sector bound nonlinearity has been considered for frequency domain stability analysis of linear single-input single- output (SISO) and decoupled multi-input multi-output (MIMO) systems associated with the fuzzy logic controller.
Abstract: The concept of the sector bound nonlinearity has been considered for frequency domain stability analysis of linear single-input single-output (SISO) and decoupled multi-input multi-output (MIMO) systems associated with the fuzzy logic controller. For analysis of the fuzzy logic controller, the system's model is considered to be available in transfer-function form, but for generation of the fuzzy logic controller no model description is necessary. A generalized Nyquist-type stability analysis is presented.
150 citations
TL;DR: The properties of binary operations in a real interval are considered and used in the discussion of generalized operations on fuzzy sets, on fuzzy numbers and on fuzzy probabilistic sets.
124 citations
Book•
01 Jan 1984
TL;DR: [B95] Boman, M.
Abstract: [B95] Boman, M.: " Rational Decisions and Multi-Agent Systems " in Proc.
123 citations
TL;DR: A general query language, FRIL, which uses fuzzy base relations and rewrite rules is described and incorporates an automated fuzzy inference mechanism and should find applications in many areas of knowledge engineering such as expert systems, linguistic controllers, etc.
121 citations
34 citations
TL;DR: Some properties of fuzzy systems described by fuzzy-relation equations are investigated, focusing on energy and entropy measures of fuzziness of solutions of the class of equations under discussion.
30 citations
TL;DR: The determination of a family of fuzzy relations of the system is described in detail and several identification problems in fuzzy systems are considered by means of fuzzy relational equations.
17 citations
TL;DR: Some properties concerning subinverses and regularity of fuzzyMatrices are given and the largest subinverse is shown by the properties and the results are considered to be useful for the theory of fuzzy matrices.
17 citations
TL;DR: The algorithm selection process is formulated as a fuzzy decision-making problem and a fuzzy algorithm selection method is proposed and this method is applied to the guidance and control problem in a command and control system.
Abstract: Generally, various approaches, like optimization, stabilization, adaptation and so on, can be taken to solve a control problem. Thus, there exist several kinds of algorithms to solve a certain problem and each algorithm's characteristics depend on the approach to be taken. In the case that these algorithms are applied to practical system problems, it is necessary to examine the algorithms' effectiveness and select a suitable one among usable algorithms. On the other hand, in a man-machine system, a human operator occasionally selects the better one by use of his experimental knowhow or a priori information. But it is very difficult for the operator to make a good decision continuously in a real time system. In this paper, an on-line algorithm selection problem is considered. Firstly, the algorithm selection process is formulated as a fuzzy decision-making problem and a fuzzy algorithm selection method is proposed. Ifthen type rules are used to reason the effectiveness of the algorithms. A priori information and know-how is utilized in the reasoning rules. Secondly, this method is applied to the guidance and control problem in a command and control system. Finally, simulation is conducted to evaluate the effectiveness of the proposed method.
TL;DR: This formulation of Fuzzy Linear Programming (FLP) Problem with fuzzy coeffieients by the extension principle seems tractable and applicable to real world decision problem where human estimation is influential.
Abstract: FORMULATION OF FUZZY LINEAR PROGRAMMING PROBLEM BASED ON FUZZY OBJECTIVE FUNCTION Hideo TANAKA Hidetomo ICHlHASHI University of Osaka Prefecture University of Osaka Prefecture Kiyoji ASAI University of Osaka Prefecture This paper describes a formulation of Fuzzy Linear Programming (FLP) Problem with fuzzy coeffieients by the extension principle. An order relation among fuzzy sets is defined by fuzzy max which is defined through the extension principle. Mathematically speaking, it means that The constraints and the object are both fuzzified by fuzzy linear function. Two FLP problems are considered as follows: (i) Problem tA) is to decide the non-fuzzy solution x that maximizes t subject t t (ii) Problem ¥=c;:x to ~ ~ ~ and (B) is to decide the fuzzy solution x that maximizes t ¥=c~ subject to kI::t::.~t. This fuzzy solution means the possibility distribution of solution in the problem(B) . Two concepts of optimality are used as maximizing the fuzzy objective function in a whole sense of fuzzy SEt and minimizing its fuzziness. Since the FLP problem (A) takes the possibility distribution of coefficients into consideration, its solution is robust to the uncertainty of model, compared with the solution in conventional LP problem. The FLP problem (B) provides us with the possibility of solution which reflects the fuzziness of paramHers. This formulation can be used as a model of top level decision problem in a fuzzy environment. This approach seems tractable and applicable to thE! real world decision problem where human estimation is influential. Fuzzy sets are restricted to a class of trianguler membership functions. Owing to this simplification, the FLP problem can be turned into a conventional LP problem with twice numbers of constraints in the FLP problem. Numerical examples are discribed to explain our FLP problems. © 1984 The Operations Research Society of Japan
TL;DR: Fuzzy symbols are defined as particular fuzzy sets whose membership functions operate between two linearly ordered spaces, and the operations of maximum and of minimum between two fuzzy symbols are studied.
01 Dec 1984
TL;DR: A mathematical description of a fuzzy dynamic system will be developed; a systematic method to derive a fuzzy controller strategy from an underlying fuzzy system model using fuzzy inverse relations will be established.
Abstract: A mathematical description of a fuzzy dynamic system will be developed; a systematic method to derive a fuzzy controller strategy from an underlying fuzzy system model using fuzzy inverse relations will also be established. The theoretical development presented here enables the suboptimal Control of the fuzzy system. As an illustrative example, a fuzzy controller problem with a unit delay will be discussed in detail and the simulation results will be presented.
TL;DR: Based on the experience of the operators in tuning the parameters of the widely used PI controller, a PI model reference fuzzy adaptive control system (MRFAC) is studied and the fuzzy control can be represented explicitly.
01 Jan 1984
TL;DR: The theoretical development presented here enables the suboptimal control of the fuzzy system and a systematic method to derive a fuzzy controller Strategy from an underlying fuzzy system model using fuzzy inverse relations is established.
Abstract: A mathematical description of a fuzzy dynamic System Will be developed; a systematic method to derive a fuzzy controller Strategy from an underlying fuzzy system model using fuzzy inverse relations will also established. The theoretical development presented here enables the suboptimal control of the fuzzy system. an illustrative example, a fuzzy controller problem with a unit delay will be discussed in detail and the simulation results will be presented.