A new convergence condition for discrete-time nonlinear system identification using a hopfield neural network

Wei Yen Wang, I. Hsum Li, Wei Ming Wang, Shun Feng Su, Nai Jian Wang

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

This paper presents a method of discrete-time nonlinear system identification using a Hopfield neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. A linear combination of Gaussian basis functions is used to replace the nonlinear function of the equivalent discrete-time nonlinear system. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model. Using the outputs of the HNN, one can obtain the optimized coefficients of the linear combination of Gaussian basis functions conditional on properly choosing an activation function scaling factor of the HNN. The main contributions of this paper is that the convergence of learning of the HNN can be guaranteed if the activation function scaling factor is properly chosen. Finally, to demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.

Original languageEnglish
Pages (from-to)685-689
Number of pages5
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume1
Publication statusPublished - 2005 Dec 2
EventIEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States
Duration: 2005 Oct 102005 Oct 12

Keywords

  • Discrete hopfield learning model
  • Gradient descent learning
  • Hopfield neural network

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'A new convergence condition for discrete-time nonlinear system identification using a hopfield neural network'. Together they form a unique fingerprint.

Cite this