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Multiobjective Nonlinear Identification

  • G. P. Liu
Part of the Advances in Industrial Control book series (AIC)

Abstract

The identification of nonlinear systems can be posed as a nonlinear functional approximation problem. From the Weierstrass Theorem (Powell, 1981) and the Kolmogorov theorem (Sprecher, 1965) in approximation theory, it is shown that the polynomial and many other approximation schemes can approximate a continuous function arbitrarily well. In recent years, a number of nonlinear system identification approaches, particularly identification using neural networks, based on the universal approximation theorem (Cybenko, 1989), are applications of a similar mathematical approach.

Keywords

Genetic Algorithm Basis Function Performance Function Good Chromosome Gaussian Radial Basis 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 London 2001

Authors and Affiliations

  • G. P. Liu
    • 1
  1. 1.School of Mechanical Materials, Manufacturing Engineering and ManagementUniversity of NottinghamNottinghamUK

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