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A knowledge-based cooperative differential evolution for neural fuzzy inference systems

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Abstract

This study presents a knowledge-based cooperative differential evolution (KCoDE) for neural fuzzy inference systems to solve nonlinear control system problems. KCoDE decomposes the fuzzy system into subpopulations, and each individual within each subpopulation evolves separately. The KCoDE method uses five mutation strategies of differential evolution as the knowledge sources to generate a new population space to influence the population space. The exemplary individuals are selected from the population space to the belief space. The belief space in KCoDE is the information repository in which individuals can store their experiences to guide others. Finally, the experimental results show that the proposed KCoDE method better approximates the global optimal solution and has a faster convergence rate than the other methods.

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Acknowledgments

This study was sponsored by the National Science Council, Taiwan, R.O.C., under Grant NSC 101-2221-E-150-085.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Formosa University, No.64, Wunhua Rd., Huwei Township, Yunlin County, 632, Taiwan

    Cheng-Hung Chen & Sheng-Yen Yang

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  1. Cheng-Hung Chen
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  2. Sheng-Yen Yang
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Correspondence to Cheng-Hung Chen.

Additional information

Communicated by Y. Jin.

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Chen, CH., Yang, SY. A knowledge-based cooperative differential evolution for neural fuzzy inference systems. Soft Comput 17, 883–895 (2013). https://doi.org/10.1007/s00500-012-0959-5

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