A GMDH-based fuzzy modeling approach for constructing TS model

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...

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Bibliographic Details
Published in:Fuzzy sets and systems 2012-02, Vol.189 (1), p.19-29
Main Authors: Zhu, Bing, He, Chang-Zheng, Liatsis, Panos, Li, Xiao-Yu
Format: Article
Language:English
Subjects:
Complexity
Furnaces
Fuzzy
Fuzzy logic
Fuzzy modeling
Fuzzy set theory
GMDH
Group method of data handling (GMDH)
Learning
Noise immunity
Optimization
Takagi–Sugeno model
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Summary: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. ► A new learning algorithm based on GMDH is proposed for the identification of TS fuzzy model. ► The new approach can identify the structure of TS model automatically. ► Experiments on Box–Jenkins gas and UCI datasets are done in comparison with ANFIS. ► Results show the new method can achieve satisfactory results and is more robust to noise.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2011.08.004