Results 271 to 280 of about 5,525 (307)
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Fuzzy dynamic system and fuzzy linguistic controller classification

Automatica, 1994
Abstract This paper presents the classification of fuzzy dynamic systems and fuzzy linguistic controllers into standard types (TYPE 1 through TYPE 7). The need and utility value behind this classification are given. Hopefully, this classification will lead to new designs and improve the performance of control systems.
Paul P. Wang, Ching-Yu Tyan
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Fuzzy Dynamical Systems

2016
This chapter presents an introduction to fuzzy dynamical systems both continuous and discrete. To study the dynamical case, the concept of fuzzy derivative and fuzzy integral are presented. Several kinds of derivatives are explored and consequently, several types of fuzzy differential equations are studied.
LaĆ©cio Carvalho de Barros   +2 more
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Fuzzy System Dynamics

2014
In the presence of fuzzy or linguistic and dynamic variables, dynamic modeling of real-world systems is a challenge to many decision makers. In such environments with fuzzy time-dependent variables, the right decisions and the impacts of possible actions are not precisely known. The presence of linguistic variables in a dynamic environment is a serious
Michael Mutingi, Charles Mbohwa
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A description of the dynamic behavior of fuzzy systems

IEEE Transactions on Systems, Man, and Cybernetics, 1989
An approach is presented for analyzing the global behavior of a fuzzy dynamical system that applies the concept and method of cell-to-cell mapping to obtain the evolving trend of the states of a fuzzy dynamical system. The behavior of the fuzzy system is characterized by equilibria, periodic motions, and their domain of attractions.
Yung-Yaw Chen, Tsu-Chin Tsao
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Energetistic stability of fuzzy dynamic systems

IEEE Transactions on Systems, Man, and Cybernetics, 1985
A notion of stability of fuzzy dynamical systems governed by a fuzzy relational equation \(X_{k+1}=U_ k\circ X_ k\circ R\) with \(U_ k\) being a fuzzy control, \(X_ k\), \(X_{k+1}\) forming fuzzy state sets and R denoting a fuzzy relation (viz. \(U_ k:U\to [0,1]\), \(X_ k\), \(X_{k+1}:X\to [0,1]\), R:\(U\times X\times X\to [0,1])\) is discussed.
Jerzy B. Kiszka   +2 more
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General fuzzy models for dynamical systems

2015 27th International Conference on Microelectronics (ICM), 2015
Fuzzy logic systems can approximate any nonlinear function and they can also be successfully applied to system modeling. This paper describes an efficient fuzzy identification method which can be applied to nonlinear dynamical systems. The proposed method is constituted of three steps: in the first step, an ordinary fuzzy identification technique is ...
Kheireddine Chafaa   +2 more
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Compression of Dynamic Fuzzy Relation Information Systems [PDF]

open access: possibleFundamenta Informaticae, 2015
The notion of homomorphism, as an important tool for studying the relationship between information systems, has attracted a great deal of attention in recent years, and the authors tend to pay their attention to static information systems in the existing studies.
Mingjie Cai, Qingguo Li
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Fuzzy dynamic systems

1994
The aim of this paper is to study fuzzy dynamic systems. The role of fuzzy sets in formation of a conceptual and computational platform for symbolic and numerical information processing is identified. We summarize essential properties of fuzzy partitions developed with the aid of families of fuzzy sets.
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Fuzzy stopping in continuous-time dynamic fuzzy systems

Fuzzy Sets and Systems, 2002
The paper discusses a stopping problem in continuous-time dynamic fuzzy system, using fuzzy stopping times, under some different estimation of fuzzy rewards. The paper develops fuzzy stopping times in a continuous time fuzzy system. Also the paper presents the optimality equations for the optimal fuzzy rewards in terms of variational inequalities.
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Selective dynamic fuzzy neural system

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
Fuzzy neural fusion technology has solved many problems and been applied in different various ways. It can produce both learning ability and uncertain knowledge processing ability. However, for a more powerful intelligent system, it is necessary to consider the human selective recognition dynamic processing and the hierarchical structure of knowledge ...
Jeong Yon Shim, Chong Sun Hwang
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