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Designing Neuro-Fuzzy Systems through Backpropagation

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Fuzzy Modelling

Part of the book series: International Series in Intelligent Technologies ((ISIT,volume 7))

Abstract

The goal of neuro-fuzzy combinations is to obtain adaptive systems that can use prior knowledge, and can be interpreted by means of linguistic rules. Neuro-fuzzy models can be divided into cooperative models, which use neural networks to determine fuzzy system parameters, and hybrid models which create a new architecture using concepts from both worlds. Besides this, there are concurrent neural/fuzzy models that use neural networks and fuzzy systems separately. Most approaches adapt the backpropagation learning rule [33] for neural networks. Some of these systems are discussed in the following pages.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Technical University of Braunschweig, Bueltenweg 74-75, D-38106, Braunschweig, Germany

    Detlef Nauck & Rudolf Kruse

Authors
  1. Detlef Nauck
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  2. Rudolf Kruse
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 2N2, Canada

    Witold Pedrycz

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© 1996 Kluwer Academic Publishers

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Nauck, D., Kruse, R. (1996). Designing Neuro-Fuzzy Systems through Backpropagation. In: Pedrycz, W. (eds) Fuzzy Modelling. International Series in Intelligent Technologies, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1365-6_10

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  • DOI: https://doi.org/10.1007/978-1-4613-1365-6_10

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8589-2

  • Online ISBN: 978-1-4613-1365-6

  • eBook Packages: Springer Book Archive

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