A GMDH-based fuzzy modeling approach for constructing TS model

Authors:
Bing Zhu;Chang-Zheng He;Panos Liatsis;Xiao-Yu Li
Affiliations:
Business School, Sichuan University, Chengdu 610064, PR China;Business School, Sichuan University, Chengdu 610064, PR China;School of Engineering and Mathematical Sciences, City University, London EC1V 0HB, UK;Business School, Sichuan University, Chengdu 610064, PR China
Venue:
Fuzzy Sets and Systems
Year:
2012

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Abstract

In this paper, a new learning algorithm based on group method of data handling (GMDH) is proposed for the identification of Takagi-Sugeno fuzzy model. Different from existing methods, the new approach, called TS-GMDH, starts from simple elementary TS fuzzy models, and then uses the mechanism of GMDH to produce candidate fuzzy models of growing complexity until the TS model of optimal complexity has been created. The main characteristic of the new approach is its ability to identify the structure of TS model automatically. Experiments on Box-Jenkins gas furnace data and UCI datasets have shown that the proposed method can achieve satisfactory results and is more robust to noise in comparison with other TS modeling techniques such as ANFIS.