A Fuzzy Neural Network for Approximate Fuzzy Reasoning

Liam P. Maguire, T. Martin McGinnity and Liam J. McDaid
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Liam P. Maguire: University of Ulster, Intelligent Systems Engineering Laboratory Faculty of Engineering
T. Martin McGinnity: University of Ulster, Intelligent Systems Engineering Laboratory Faculty of Engineering
Liam J. McDaid: University of Ulster, Intelligent Systems Engineering Laboratory Faculty of Engineering

Chapter 2 in Intelligent Hybrid Systems, 1997, pp 35-58 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 1997
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DOI: 10.1007/978-1-4615-6191-0_2

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