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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model
verfasst von : Shang-Ming Zhou, John Q. Gan
Erschienen in: Multi-Objective Machine Learning
Verlag: Springer Berlin Heidelberg
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This chapter discusses the interpretability of Takagi-Sugeno (TS) fuzzy systems. A new TS fuzzy model, whose membership functions are characterized by linguistic modifiers, is presented. The tradeoff between global approximation and local model interpretation has been achieved by minimizing a multiple objective performance measure. In the proposed model, the local models match the global model well and the erratic behaviors of local models are remedied effectively. Furthermore, the transparency of partitioning of input space has been improved during parameter adaptation.