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Evolutionary Approaches to Rule Extraction for Fuzzy Logic Controllers

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Advances in Soft Computing — AFSS 2002 (AFSS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2275))

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Abstract

We first discuss some limitations of Chan et al.’s [5] method and propose some modifications on their ‘optimized fuzzy logic controller’ (OFLC) to eliminate those limitations. Then we propose a new method to reduce the number of rules in a symmetric rulebase which reduces the search space as well as the design time. Our fitness function can reduce the number of rules maintaining the performance of the rulebase. It requires no prior knowledge about the system. Applying this procedure to the inverted pendulum problem, we get a rulebase containing less than 3% of all possible fuzzy rules and it takes about 42 steps on average to balance over the entire input space. Our results are compared with those of Lim et al.’s [4] method.

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References

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Author information

Authors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B. T. Road, 700035, Calcutta, India

    Tandra Pal

Authors
  1. Tandra Pal
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Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

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© 2002 Springer-Verlag Berlin Heidelberg

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Pal, T. (2002). Evolutionary Approaches to Rule Extraction for Fuzzy Logic Controllers. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_57

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  • DOI: https://doi.org/10.1007/3-540-45631-7_57

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43150-3

  • Online ISBN: 978-3-540-45631-5

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