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Fuzzy and Neuro-Fuzzy Models

  • Chapter
Nonlinear System Identification

Abstract

This chapter introduces fuzzy systems and how they can be cast into neuro-fuzzy system, which can be learned from data. It discusses how prior knowledge can be incorporated and how knowledge can be extracted from a data-driven neuro-fuzzy model. Different kinds of fuzzy systems are analyzed where the Takagi-Sugeno variant already builds bridges to the subsequent chapters on local linear modeling approaches. Different learning schemes for neuro-fuzzy models are discussed, and their principal ideas highlighted. The role of “defuzzification” or normalization in the context of learning and interpretability is discussed in detail.

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  1. University of Siegen, Netphen, Germany

    Oliver Nelles

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  1. Oliver Nelles
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Nelles, O. (2020). Fuzzy and Neuro-Fuzzy Models. In: Nonlinear System Identification. Springer, Cham. https://doi.org/10.1007/978-3-030-47439-3_12

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