Abstract
This paper proposes a method which identifies unknown systems using fuzzy-rule based models(fuzzy models) when the input-output pairs of the system are given. It searches fuzzy models by genetic algorithms based on the given input-output pairs. The method finds all parameters of fuzzy models : the number and the position of the fuzzy sets of each input and the rule base- We encode only the fuzzy partitions of inputs into chromosomes, and then generate fuzzy rules from the encoded fuzzy partitions and the given data. We evaluate the performance with 3 functions. The experiments show that the proposed method properly locates the fuzzy sets on the input domains and generates the fuzzy rules approximating the given data.
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© 1997 Springer-Verlag Berlin Heidelberg
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Lee, JH., Lee-Kwang, H. (1997). Fuzzy identification of unknown systems based on GA. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028538
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DOI: https://doi.org/10.1007/BFb0028538
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