Nonlinear System Identification with Neurofuzzy Methods

Oliver Nelles
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Oliver Nelles: Technische Hochschule Darmstadt, Institut für Regelungstechnik

Chapter 12 in Intelligent Hybrid Systems, 1997, pp 283-310 from Springer

Abstract: 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.

Keywords: Membership Function; Fuzzy System; Fuzzy Model; Validity Function; Gaussian Membership Function (search for similar items in EconPapers)
Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6191-0_12

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DOI: 10.1007/978-1-4615-6191-0_12

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