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Fuzzy Neuro Systems: An Overview

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Fuzzy-Systems in Computer Science

Part of the book series: Artificial Intelligence / Künstliche Intelligenz ((CI))

Abstract

This paper gives an overview to different concepts of neural fuzzy systems. There are already several approaches to combine neural networks and fuzzy systems, to obtain adaptive systems that can use prior knowledge and that can be interpreted by means of linguistic rules as they are used e.g. in fuzzy controllers. Neural fuzzy models can be divided in two classes: Cooperative models which use neural nets and fuzzy systems separately, and hybrid models which create a new architecture using concepts from both worlds. Several of these approaches are discussed in this paper.

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Authors
  1. Detlef Nauck
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Editor information

Editors and Affiliations

  1. Institut für Betriebssysteme und Rechnerverbund, Technische Universität Braunschweig, Bültenweg 74/75, 38106, Braunschweig, Germany

    Jörg Gebhardt

  2. Siemens AG, ZFE ST SN 43, Otto-Hahn-Ring 6, 81739, München, Germany

    Rainer Palm

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© 1994 Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden

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Nauck, D. (1994). Fuzzy Neuro Systems: An Overview. In: Kruse, R., Gebhardt, J., Palm, R. (eds) Fuzzy-Systems in Computer Science. Artificial Intelligence / Künstliche Intelligenz. Vieweg+Teubner Verlag. https://doi.org/10.1007/978-3-322-86825-1_7

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  • DOI: https://doi.org/10.1007/978-3-322-86825-1_7

  • Publisher Name: Vieweg+Teubner Verlag

  • Print ISBN: 978-3-322-86826-8

  • Online ISBN: 978-3-322-86825-1

  • eBook Packages: Springer Book Archive

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