Comparison of Fuzzy and Neural Systems for Implementation of Nonlinear Control Surfaces

  • T. T. Xie
  • H. Yu
  • B. M. Wilamowski
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 99)

Abstract

In this paper, a comparison between different fuzzy and neural systems is presented. Instead of using traditional membership functions, such as triangular, trapezoidal and Gaussian, in fuzzy systems, the monotonic pair-wire or sigmoidal activation function is used for each neuron. Based on the concept of area selection, the neural systems can be designed to implement the identical properties like fuzzy systems have. All parameters of the proposed neural architecture are directly obtained from the specified design and no training process is required. Comparing with traditional neuro-fuzzy systems, the proposed neural architecture is more flexible and simplifies the design process by removing division/normalization units.

Keywords

Membership Function Fuzzy System Fuzzy Rule Neural System Fuzzy Variable 
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 2012

Authors and Affiliations

  • T. T. Xie
    • 1
  • H. Yu
    • 1
  • B. M. Wilamowski
    • 1
  1. 1.Department of Electrical and Computer EngineeringAuburn UniversityAuburnUSA

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