Complex neuro-fuzzy self-learning approach to function approximation

Chunshien Li, Tai Wei Chiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

4 引文 斯高帕斯(Scopus)


A new complex neuro-fuzzy self-learning approach to the problem of function approximation is proposed, where complex fuzzy sets are used to design a complex neuro-fuzzy system as the function approximator. Particle swarm optimization (PSO) algorithm and recursive least square estimator (RLSE) algorithm are used in hybrid way to adjust the free parameters of the proposed complex neuro-fuzzy systems (CNFS). The hybrid PSO-RLSE learning method is used for the CNFS parameters to converge efficiently and quickly to optimal or near-optimal solution. From the experimental results, the proposed CNFS shows better performance than the traditional neuro-fuzzy system (NFS) that is designed with regular fuzzy sets. Moreover, the PSO-RLSE hybrid learning method for the CNFS improves the rate of learning convergence, and shows better performance in accuracy. Three benchmark functions are used. With the performance comparisons shown in the paper, excellent performance by the proposed approach has been observed.

主出版物標題Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings
版本PART 2
出版狀態已出版 - 2010
事件2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam
持續時間: 24 3月 201026 3月 2010


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 2
5991 LNAI


???event.eventtypes.event.conference???2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010
國家/地區Viet Nam
城市Hue City


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