A Hammerstein-Wiener recurrent neural network with universal approximation capability

Jeen-Shing Wang, Yi Chung Chen

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

This paper presents a Hammerstein-Wiener recurrent neural network with a parameter learning algorithm for identifying unknown dynamic nonlinear systems. The proposed recurrent neural network resembles the conventional Hammerstein-Wiener model that consists of a dynamic linear subsystem embedded between two static nonlinear subsystems. There are two novelties in our network: 1) the three subsystems are integrated into a single recurrent neural network whose output is the nonlinear transformation of a linear state-space equation; 2) the well-developed linear theory can be applied directly to the linear subsystem of the trained network to analyze its characteristics. In addition, we utilized the Stone-Weierstrass theorem to demonstrate the proposed network possesses the universal approximation capability. Finally, a computer simulation and comparisons with some existing models have been conducted to demonstrate the effectiveness of the proposed network and its parameter learning algorithm.

Original languageEnglish
Article number4811555
Pages (from-to)1832-1837
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 2008 Oct 122008 Oct 15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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