A fully automated recurrent neural network for unknown dynamic system identification and control

Jeen Shing Wang, Yen Peng Chen

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)

Abstract

This paper presents a fully automated recurrent neural network (FARNN) that is capable of self-structuring its network in a minimal representation with satisfactory performance for unknown dynamic system identification and control. A novel recurrent network, consisting of a fully-connected single-layer neural network and a feedback interconnected dynamic network, was developed to describe an unknown dynamic system as a state-space representation. Next, a fully automated construction algorithm was devised to construct a minimal state-space representation with the essential dynamics captured from the input-output measurements of the unknown system. The construction algorithm integrates the methods of minimal model determination, parameter initialization and performance optimization into a systematic framework that totally exempt trial-and-error processes on the selections of network sizes and parameters. Computer simulations on benchmark examples of unknown nonlinear dynamic system identification and control have successfully validated the effectiveness of the proposed FARNN in constructing a parsimonious network with superior performance.

Original languageEnglish
Pages (from-to)1363-1372
Number of pages10
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume53
Issue number6
DOIs
Publication statusPublished - 2006 Jun 1

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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