標題: | DYNAMIC SYSTEM IDENTIFICATION USING HIGH-ORDER HOPFIELD-BASED NEURAL NETWORK (HOHNN) |
作者: | Wang, Chi-Hsu Hung, Kun-Neng 電機工程學系 Department of Electrical and Computer Engineering |
關鍵字: | Hopfield neural network;system identification;functional link net;Lyapunov theorem;robust learning analysis |
公開日期: | 1-十一月-2012 |
摘要: | The high-order Hopfield-based neural network (HOHNN) with functional link net (FLN) has been developed in this paper for the purpose of uncertain dynamic system identification. In comparison with the traditional Hopfield neural network (HNN) and the high-order neural network (HONN), the compact structure of FLN, with a systematic order mathematical representation combined into the proposed HOHNN, has additional inputs for each neuron for faster convergence rate and less computational load. The weighting factors in HOHNN are tuned via the Lyapunov stability theorem to guarantee the convergence performance of real-time system identification. The robust learning analysis of HOHNN to improve the convergence in the performance is also discussed. The simulation results and computation analysis for different Hopfield-based neural networks are conducted to show the effectiveness of HOHNN in uncertain dynamic system identification. |
URI: | http://dx.doi.org/10.1002/asjc.495 http://hdl.handle.net/11536/20630 |
ISSN: | 1561-8625 |
DOI: | 10.1002/asjc.495 |
期刊: | ASIAN JOURNAL OF CONTROL |
Volume: | 14 |
Issue: | 6 |
起始頁: | 1553 |
結束頁: | 1566 |
顯示於類別: | 期刊論文 |