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2008

Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

11 years 22 days ago
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
Abstract--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...
Cheng-Jian Lin, Cheng-Hung Chen, Chin-Teng Lin
Added 29 Dec 2010
Updated 29 Dec 2010
Type Journal
Year 2008
Where TFS
Authors Cheng-Jian Lin, Cheng-Hung Chen, Chin-Teng Lin
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