NDLI: A GA-based method for constructing TSK fuzzy rules from numerical data

Content Provider	IEEE Xplore Digital Library
Author	Kumar, A. Agrawal, D.P. Joshi, S.D.
Copyright Year	2003
Description	Author affiliation: ABV Indian Inst. of Inf. Technol. & Manage., Gwalior, India (Kumar, A.)
Abstract	A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.
Sponsorship	IEEE IEEE Neurla Networks Soc
Starting Page	131
Ending Page	136
File Size	412534
Page Count	6
File Format	PDF
ISBN	0780378105
DOI	10.1109/FUZZ.2003.1209350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Fuzzy sets Takagi-Sugeno-Kang model Equations Information technology Information management Technology management Genetic algorithms Predictive models Economic forecasting
Content Type	Text
Resource Type	Article

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