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Nonlinear System Identification with Neurofuzzy Methods

In: Intelligent Hybrid Systems

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  • Oliver Nelles

    (Technische Hochschule Darmstadt, Institut für Regelungstechnik)

Abstract

This chapter discusses nonlinear system identification with neurofuzzy methods. In a general part, summary and overview of the most important types of fuzzy models are given. Their properties, advantages, and drawbacks are illustrated. In a more specific part a new algorithm for the construction of Takagi-Sugeno fuzzy systems is presented in detail. It is successfully applied to the identification of two nonlinear dynamic real-world processes.

Suggested Citation

  • Oliver Nelles, 1997. "Nonlinear System Identification with Neurofuzzy Methods," Springer Books, in: Da Ruan (ed.), Intelligent Hybrid Systems, chapter 12, pages 283-310, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6191-0_12
    DOI: 10.1007/978-1-4615-6191-0_12
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