Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN

  • N. Sundararajan
  • P. Saratchandran
  • Yan Li
Chapter
Part of the The Springer International Series on Asian Studies in Computer and Information Science book series (ASIS, volume 12)

Abstract

In recent years, nonlinear system identification using neural network has become a widely studied area because of its close relationship to the system control. Basically, any good identification scheme that incorporates RBFN should satisfy two criteria: (i) The parameters of the RBFN are tuned appropriately so that its output to an input signal can approximate the response of the real system to the same input with good accuracy. (ii) The network structure is compact and the parameter adaptive law is efficient so that fast on-line learning can be implemented.

Keywords

Hide Neuron Extend Kalman Filter Radial Basis Function Neural Network Dead Zone Hide Unit 
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 2002

Authors and Affiliations

  • N. Sundararajan
    • 1
  • P. Saratchandran
    • 1
  • Yan Li
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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