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Neuro-Fuzzy Architectures Based on the Logical Approach

  • Danuta Rutkowska
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 85)

Abstract

The fuzzy inference neural networks (see Section 3.3) that realize the in-ference based on the logical approach are the subject of this chapter. Firstly,in Section 5.1, the mathematical descriptions of the neuro-fuzzy systemsemploying different fuzzy implications are determined. Then, the connec-tionist, multi-layer, architectures, which correspond to the implicationbased systems, are presented. These architectures are proposed in [366].The neuro-fuzzy systems of this kind are considered in [367], [430], [433], and also in the papers that refer to a specific implication, e.g. [429]. In Section 5.4, the performance analysis of the implication-based systems is illustrated. The results of computer simulations with regard to examples of function approximation, control, and classification problems, are portrayed in Section 5.5. In order to train the systems, gradient, genetic, or hybrid al-gorithms can be applied. The learning methods are described in Chapter 6. In particular, the architecture-based learning, outlined in Section 6.1.3, is recommended.

Keywords

Membership Function Fuzzy System Logical Approach Inverted Pendulum Triangular Membership Function 
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

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Danuta Rutkowska
    • 1
  1. 1.Department of Computer EngineeringTechnical University of CzestochowaCzestochowaPoland

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