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  Evolutionary Design of Fuzzy Control Systems - An Hybrid Approach (2000) [5 citations — 5 self]

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by Ajith Abraham, Baikunth Nath
The Sixth International Conference on Control, Automation, Robotics and Vision, (ICARCV
http://www-mugc.cc.monash.edu.au/~abrahamp/icarcv2000paper2.pdf
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Abstract:

Fuzzy controllers are represented by if-then rules and thus can provide a user friendly and understandable knowledge representation. Evolutionary algorithms have been widely used for optimal design of fuzzy controllers. In this paper, we first present the state-of-art adaptive fuzzy control design using evolutionary algorithms and then attempt to enhance the performance of the simple genetic search by formulating a meta-heuristic approach using Simulated Annealing (SA) and Tabu Search (TS). The proposed meta-heuristic can be regarded as a general framework for adaptive systems, i.e., systems that can change their membership functions and fuzzy rules according to different environments without human intervention.

Citations

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27 Fuzzy Logic in Control – Jager - 1995
3 Hybrid Heuristics for Optimal Design – Abraham, Nath - 2000
2 The Role of Fuzzy Logic in Evolutionary Robotics, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, A. Saffiotti and D. Driankov (Ed – Hoffmann - 1999
2 A New Simulated Annealing Algorithm – unknown authors - 1995
1 Derivation of fuzzy logic control rules from human operators control actions – Takagi, Sugeno - 1983
1 Generating Fuzzy Rules by Genetic Algorithms – Mohammadian, Stonier - 1994
1 Fuzzy / Multiobjective Genetic Systems for – Lee, Esbensen - 1997
1 Automatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms – G - 1994
1 Taillard E.and De Werra D., A User's Guide to Tabu Search – Glover - 1993
1 Development of If-Then Rules with the Use of DNA – Fumhashi - 1997
1 Lozano M and Veregay L., Tuning Fuzzy Logic Control by Genetic Algorithms – Herrera - 1995
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