Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

Cheng-ian Lin, Cheng-Hung Chen, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust-the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.
Original languageEnglish
Pages (from-to)1476-1490
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Issue number6
StatePublished - Dec 2008


  • Cooperative particle swarm optimization (CPSO); cultural algorithm (CA); elite-based structure strategy (ESS); neurofuzzy inference system (NFIS); symbiotic evolution

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