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Soft Computing

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01.05.2013 | Methodologies and Application

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

verfasst von: Cheng-Hung Chen, Sheng-Yen Yang

Erschienen in: Soft Computing | Ausgabe 5/2013

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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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Titel: A knowledge-based cooperative differential evolution for neural fuzzy inference systems
verfasst von: Cheng-Hung Chen
Sheng-Yen Yang
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0959-5

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