NDLI: Identification of fuzzy model using evolutionary programming and least squares estimate

Content Provider	IEEE Xplore Digital Library
Author	Bin Ye Chuangxin Guo Yijia Cao
Copyright Year	2004
Description	Author affiliation: Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China (Bin Ye)
Abstract	A novel hybrid algorithm EPLSE is proposed to design fuzzy rule bases automatically, which is based on the combination of EP (evolutionary programming) and LSE (least squares estimate). By utilizing the consequent parameters of the super 1/sup st/-order Sugeno model, the training error is decreased greatly. Compared with the original work, the proposed algorithm has remarkably improved the fuzzy model's precision and simplified its structure. In the simulation, EPLSE is employed to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed. Other promising applications of the proposed EPLSE are also suggested.
Starting Page	593
Ending Page	598
File Size	362953
Page Count	6
File Format	PDF
ISBN	0780383532
ISSN	10987584
DOI	10.1109/FUZZY.2004.1375463
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Least squares approximation Fuzzy sets Fuzzy systems Fuzzy control Fuzzy reasoning Educational institutions Electronic mail Algorithm design and analysis Automatic programming
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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