Dynamic nonlinear system identification using a wiener-type recurrent network with OKID algorithm

Jeen Shing Wang, Yu Liang Hsu

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This paper presents a novel Wiener-type recurrent neural network with the observer/Kalman filter identification (OKID) algorithm for unknown dynamic nonlinear system identification. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our approach include: (1) the realization of a conventional Wiener model into a simple connectionist recurrent network whose output can be expressed by a nonlinear transformation of a linear state-space equation; (2) the overall network structure can be determined by the OKID algorithm effectively using only the input-output measurements; and (3) the proposed network is capable of accurately identifying nonlinear dynamic systems using fewer parameters. Computer simulations and comparisons with some existing recurrent networks and learning algorithms have successfully confirmed the effectiveness and superiority of the proposed Wienertype network with the OKID algorithm.

Original languageEnglish
Pages (from-to)891-905
Number of pages15
JournalJournal of Information Science and Engineering
Issue number3
Publication statusPublished - 2008 May 1

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics


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