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Designing Neuro-Fuzzy Systems through Backpropagation

  • Detlef Nauck
  • Rudolf Kruse
Chapter
Part of the International Series in Intelligent Technologies book series (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.

Keywords

Membership Function Fuzzy System Fuzzy Rule Fuzzy Controller Linguistic Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Detlef Nauck
    • 1
  • Rudolf Kruse
    • 1
  1. 1.Department of Computer ScienceTechnical University of BraunschweigBraunschweigGermany

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