An improved PE algorithm for continuous nonlinear system identification with application to learning in neural networks

Pingyuan Cui*, Hutao Cui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A new approximating sensitivity algorithm in prediction error method is derived for a class continuous nonlinear dynamic system identification including training multi-layer neural networks. The algorithm can be used to approximate the gradient of output with respect to unknown parameter in a wic class of continuous-discrete nonlinear systems. The comparison between new and convention algorithm, and simulated example is included to demonstrate the effectiveness of the new algorithm.

Original languageEnglish
Pages (from-to)45-58
Number of pages14
JournalAdvances in Modelling and Analysis B
Volume44
Issue number3-4
Publication statusPublished - 2001
Externally publishedYes

Keywords

  • Approximating sensitivity
  • Continuous nonlinear system
  • Neural networks
  • Prediction error

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