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Abstract: This paper investigates the performance of Fuzzy Inference Systems having parameterized TNorms in control of robotic manipulators. The adaptation of controller parameters is carried out by Genetic Algorithms. The error and the derivative of error are utilized in the decision process. The chromosomes, which include the adjustable parameters, are updated periodically by reproduction, crossover and mutation. Conventional reproduction and crossover methods and simulated-annealing type mutation are... (Update)
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BibTeX entry: (Update)
@misc{ eskil-tnorm,
author = "M. Taner Eskil and M. Onder Efe and Okyay Kaynak",
title = "T-Norm Adaptation in Fuzzy Logic Systems Using Genetic Algorithms",
url = "citeseer.ist.psu.edu/eskil99tnorm.html" }
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18 Genetic Algorithms for Pattern Recognition (context) - Pal, Wang - 1996
17 PTR Prentice Hall (context) - Wang - 1994
12 A Comparative Study of Neural Network Structures in Identifi.. - Efe, Kaynak - 1999
11 PTR Prentice Hall (context) - Jang, Sun - 1997
11 Integrated Motions Incorporated (context) - Manipulator, User
8 IEEE Signal Processing Magazine (context) - Tang, Man et al. - 1996
6 Stable Training of Computationally Intelligent Systems By Us.. - Efe
4 A Comparative Study of Soft Computing Methodologies in Ident.. - Efe, Kaynak - 1998
3 A Novel Optimization Procedure for Training of Fuzzy Inferen.. - Efe
3 Computational Intelligence: Soft Computing and Fuzzy-Neuro I.. (context) - Kaynak, Zadeh et al. - 1998
2 An Introduction to Genetic Algorithms (context) - Grant - 1995
2 Genetic Algorithms for Function Optimization (context) - Deb - 1998
1 Generalized Conjunction and Disjunction Operators for Fuzzy .. (context) - Batyrshin, Kaynak et al. - 1998
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