Parameter identification of excitation systems based on hop-field neural network

被引：0
|
作者：
Liao, Q. F. [1 ]
Liu, D. C. [1 ]
Ying, L. M. [1 ]
Cui, X. [1 ]
Li, Y. [1 ]
He, W. T. [1 ]
机构：
[1] Wuhan Univ, Sch Elect Engn, Wuhan 430072, Peoples R China
来源：
2006 INTERNATIONAL CONFERENCE ON POWER SYSTEMS TECHNOLOGY: POWERCON, VOLS 1- 6 | 2006年
关键词：
excitation system; Hopfield neural network (BNN); Parameter estimation; State space model; System identification;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The parameter identification based on Hopfield neural network (HNN) was applied to a static excitation system. The applicable algorithm of the identification method was given in detail. Nine-parameter excitation system was studied. The HNN of twenty neurons were designed in order to identify these parameters. Finally model validation was performed. Numerical simulation results testify that this method has high precision and quick convergence. The method can be implemented with electronic circuit, so it will benefit the on-tine parameter identification of the excitation system and will have significance to any system that can be described by state space model.
引用
收藏
页码：957 / +
页数：2
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