Weight training for performance optimization in fuzzy neural network

Hui Chen Chang; Yau Tarng Juang

doi:10.1007/11552413_86

Weight training for performance optimization in fuzzy neural network

Hui Chen Chang, Yau Tarng Juang

Department of Electrical Engineering

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

Abstract

An algorithm for improving performance by training a fuzzy neural network (FNN) based on the back-propagation (BP) algorithm and grey relations is proposed. This technique is developed by directly incorporating the grey relational coefficient (GRC) into the learning rule of the BP, and a BP with GRC technique is proposed in order to improve the performance of training the FNN. From the simulation results, we demonstrate this technique applied for controlling a nonlinear fuzzy model car system and find the result is better than the classical BP algorithm for training the FNN.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	596-603
Number of pages	8
ISBN (Print)	3540288945, 9783540288947
DOIs	https://doi.org/10.1007/11552413_86
State	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 14 Sep 2005 → 16 Sep 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3681 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	14/09/05 → 16/09/05

Access to Document

10.1007/11552413_86

Cite this

Chang, H. C., & Juang, Y. T. (2005). Weight training for performance optimization in fuzzy neural network. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 596-603). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI). Springer Verlag. https://doi.org/10.1007/11552413_86

Chang, Hui Chen ; Juang, Yau Tarng. / Weight training for performance optimization in fuzzy neural network. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 596-603 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Chang, HC & Juang, YT 2005, Weight training for performance optimization in fuzzy neural network. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3681 LNAI, Springer Verlag, pp. 596-603, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 14/09/05. https://doi.org/10.1007/11552413_86

Weight training for performance optimization in fuzzy neural network. / Chang, Hui Chen; Juang, Yau Tarng.

Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 596-603 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI).

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

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N2 - An algorithm for improving performance by training a fuzzy neural network (FNN) based on the back-propagation (BP) algorithm and grey relations is proposed. This technique is developed by directly incorporating the grey relational coefficient (GRC) into the learning rule of the BP, and a BP with GRC technique is proposed in order to improve the performance of training the FNN. From the simulation results, we demonstrate this technique applied for controlling a nonlinear fuzzy model car system and find the result is better than the classical BP algorithm for training the FNN.

AB - An algorithm for improving performance by training a fuzzy neural network (FNN) based on the back-propagation (BP) algorithm and grey relations is proposed. This technique is developed by directly incorporating the grey relational coefficient (GRC) into the learning rule of the BP, and a BP with GRC technique is proposed in order to improve the performance of training the FNN. From the simulation results, we demonstrate this technique applied for controlling a nonlinear fuzzy model car system and find the result is better than the classical BP algorithm for training the FNN.

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Chang HC, Juang YT. Weight training for performance optimization in fuzzy neural network. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag. 2005. p. 596-603. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11552413_86

Weight training for performance optimization in fuzzy neural network

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