Automated nonlinear system modelling with multiple neural networks

Wen Yu, Kang Li, Xiaoou Li

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.
Original languageEnglish
Pages (from-to)1683-1695
Number of pages13
JournalInternational Journal of Systems Science
Issue number10
Publication statusPublished - Oct 2011

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications


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