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

*此作品的通信作者

研究成果: 雜誌貢獻會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)685-689
頁數5
期刊Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
1
出版狀態已發佈 - 2005
對外發佈
事件IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, 美国
持續時間: 2005 10月 102005 10月 12

ASJC Scopus subject areas

  • 工程 (全部)

指紋

深入研究「A new convergence condition for discrete-time nonlinear system identification using a hopfield neural network」主題。共同形成了獨特的指紋。

引用此