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A Fuzzy Neural Network for Approximate Fuzzy Reasoning

  • Liam P. Maguire
  • T. Martin McGinnity
  • Liam J. McDaid

Abstract

In this chapter the authors present an alternative neurofuzzy architecture for approximate fuzzy reasoning. The term approximate fuzzy reasoning is employed to highlight an approximation to the conventional fuzzy reasoning approach which considerably simplifies the resulting architecture. The performance of the fuzzy neural network is demonstrated by its application to three benchmark problems: nonlinear function approximation; on-line identification of control systems and finally chaotic time series prediction. Simulation results are presented using the MATLAB neural network toolbox and these are compared with traditional neural networks; other fuzzy neural networks and conventional fuzzy reasoning approaches. The work demonstrates the advantage of a neurofuzzy approach and highlights the advantages of this architecture for a hardware realization.

Keywords

Hide Layer Fuzzy Neural Network Fuzzy Reasoning Chaotic Time Series Input Domain 
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 Science+Business Media New York 1997

Authors and Affiliations

  • Liam P. Maguire
    • 1
  • T. Martin McGinnity
    • 1
  • Liam J. McDaid
    • 1
  1. 1.Intelligent Systems Engineering Laboratory Faculty of EngineeringUniversity of UlsterDerryUK

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