Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms

2003 ◽  
pp. 100-118 ◽  
Author(s):  
Yaochu Jin
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Systems

scholarly journals Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms

2007 ◽  
Vol 15 (3) ◽  
pp. 385-397 ◽  
Author(s):  
Yuehui Chen ◽  
Bo Yang ◽  
Ajith Abraham ◽  
Lizhi Peng
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Systems ◽  
Automatic Design ◽  
Takagi Sugeno

scholarly journals Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Biaobiao Zhang ◽  
Yue Wu ◽  
Jiabin Lu ◽  
K.-L. Du
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Structural Optimization ◽  
Evolutionary Computation ◽  
Fuzzy Systems ◽  
Parametric Optimization ◽  
Optimization Techniques ◽  
System Modelling ◽  
Discrete Optimization Problem ◽  
Conventional Optimization

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

SEARCH OF OPTIMAL MEMBERSHIP FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS. PART II. METHOD IMPLEMENTATION AND STUDY OF ITS EFFICIENCY

2021 ◽  
Vol 2 ◽  
pp. 139-159
Author(s):  
Olexey Kozlov ◽  
◽  
Yuri Kondratenko ◽  
Keyword(s):  
Evolutionary Algorithms ◽  
Mobile Robot ◽  
Fuzzy Systems ◽  
Parametric Optimization ◽  
High Efficiency ◽  
Fuzzy Controller ◽  
Artificial Immune Systems ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Mobile Robot Control

This article is devoted to the efficiency of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The proposed method allows finding the optimal membership functions of linguistic terms at solving the compromise problem of minimizing the objective function and reducing computational costs in the process of further parametric optimization of fuzzy systems. To study the effectiveness of the considered method in this work, the search of the optimal membership functions is conducted for a fuzzy controller of the control system of a multi-purpose mobile robot designed to move along inclined and vertical ferromagnetic surfaces, with the implementation of this method based on 3 bioinspired evolutionary algorithms: genetic, artificial immune systems, biogeo­graphic. The analysis of the obtained results of computer modeling showed that the usage of the proposed method of search of optimal membership functions gives the opportunity to increase significantly the efficiency of the mobile robot control, as well as to reduce the total number of parameters at further parametric optimization of linguistic terms, which confirms the high efficiency of the developed method.

SEARCH FOR OPTIMAL FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS

2021 ◽  
Vol 1 ◽  
pp. 55-75
Author(s):  
Alexey V. Kozlov ◽  
◽  
Yuri P. Kondratenko ◽  
Keyword(s):  
Evolutionary Algorithms ◽  
Structural Optimization ◽  
Fuzzy Systems ◽  
Intelligent Systems ◽  
Information Technologies ◽  
Parametric Optimization ◽  
Computer Hardware ◽  
Search Problem ◽  
Membership Functions ◽  
Step Method

Contemporary research in the field of creation and development of intelligent systems based on fuzzy logic is carried out mainly in the direction of developing highly efficient methods for their synthesis and structural-parametric optimization. In recent years, due to the intensive development of information technologies and computer hardware, bioinspired intelligent techniques of global search are quite promising for solving problems of synthesis and optimization of fuzzy systems, which include evolutionary and swarm methods, that simulate the processes of natural selection, as well as collective behavior of various groups of social animals, insects and microorganisms in nature. This paper is devoted to the development and study of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The obtained method allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multicriteria structural optimization of various fuzzy systems in order to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. In the proposed method for finding the global optimum of the problem being solved, the iterative procedures are carried out on the basis of combination of several different bioinspired evolutionary algorithms with subsequent analysis of the results obtained and the choice of the best variant of the membership function vector. The paper outlines the theoretical foundations and information model for the implementation of the computational step-by-step method for structural optimization of fuzzy systems, as well as presents various options for carrying out its search procedures. In particular, the features of the application and adaptation to the search problem to be solved of such bioinspired evolutionary algorithms as genetic, artificial immune systems and biogeographic are discussed.

H∞ control for Takagi–Sugeno fuzzy systems

2001 ◽  
Vol 32 (7) ◽  
pp. 915-924 ◽  
Author(s):  
Jun Yoneyama ◽  
Masahiro Nishikawa ◽  
Hitoshi Katayama ◽  
Akira Ichikawa
Keyword(s):  
Fuzzy Systems ◽  
Takagi Sugeno

Improvement of AODV Routing on MANETs Using Fuzzy Systems

2011 ◽  
Vol 7 (2) ◽  
pp. 102-106 ◽  
Author(s):  
Taqwa Odey Fahad ◽  
Abduladhim A. Ali
Keyword(s):  
Fuzzy Systems
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