Complex neuro-fuzzy self-learning approach to function approximation

Chunshien Li; Tai Wei Chiang

doi:10.1007/978-3-642-12101-2_30

Complex neuro-fuzzy self-learning approach to function approximation

Chunshien Li, Tai Wei Chiang

Department of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings
Pages	289-299
Number of pages	11
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-12101-2_30
State	Published - 2010
Event	2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam Duration: 24 Mar 2010 → 26 Mar 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5991 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010
Country/Territory	Viet Nam
City	Hue City
Period	24/03/10 → 26/03/10

Keywords

complex fuzzy set
complex neuro-fuzzy system (CNFS)
function approximation
machine learning
PSO
RLSE

Access to Document

10.1007/978-3-642-12101-2_30

Cite this

Li, C., & Chiang, T. W. (2010). Complex neuro-fuzzy self-learning approach to function approximation. In Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings (PART 2 ed., pp. 289-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5991 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-12101-2_30

Li, Chunshien ; Chiang, Tai Wei. / Complex neuro-fuzzy self-learning approach to function approximation. Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 2. ed. 2010. pp. 289-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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Li, C & Chiang, TW 2010, Complex neuro-fuzzy self-learning approach to function approximation. in Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5991 LNAI, pp. 289-299, 2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010, Hue City, Viet Nam, 24/03/10. https://doi.org/10.1007/978-3-642-12101-2_30

Complex neuro-fuzzy self-learning approach to function approximation. / Li, Chunshien; Chiang, Tai Wei.

Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 2. ed. 2010. p. 289-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5991 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Li C, Chiang TW. Complex neuro-fuzzy self-learning approach to function approximation. In Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 2 ed. 2010. p. 289-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-12101-2_30

Complex neuro-fuzzy self-learning approach to function approximation

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