Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules

Authors:
R. J. Kuo;S. M. Hong;Y. Lin;Y. C. Huang
Affiliations:
Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei, Taiwan 106, ROC;Department of Business Administration, National Chengchi University, No. 64, Sec. 2, ZhiNan Rd., Wenshan District, Taipei City 11605, Taiwan, ROC;Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei, Taiwan 106, ROC;Department of Accounting, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu Township, Kaohsiung County 840, Taiwan, ROC
Venue:
Neurocomputing
Year:
2008

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Abstract

This study proposes a fuzzy neural network (FNN) that can process both fuzzy inputs and outputs. The continuous genetic algorithm (CGA) is employed to enhance its performance. Both the simulation and real-world problem results show that the proposed CGA-based FNN can obtain the relationship between fuzzy inputs and outputs. CGA can not only shorten the training time but also increase the accuracy for the FNN.