Approximation of dynamical time-variant systems by continuous-time recurrent neural networks

被引：74
|
作者：
Li, XD [1 ]
Ho, JKL
Chow, TWS
机构：
[1] City Univ Hong Kong, Dept Mfg Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
[2] Sun Yat Sen Univ, Dept Elect Engn, Guangzhou, Guangdong, Peoples R China
[3] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2005年 / 52卷 / 10期
关键词：
approximation; dynamical time-variant systems; recurrent neural networks;
D O I：
10.1109/TCSII.2005.852006
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：
This paper studies the approximation ability of continuous-time recurrent neural networks to dynamical time-variant systems. It proves that any finite time trajectory of a given dynamical time-variant system can be approximated by the internal state of a continuous-time recurrent neural network. Given several special forms of dynamical time-variant systems or trajectories, this paper shows that they can all be approximately realized by the internal state of a simple recurrent neural network.
引用
收藏
页码：656 / 660
页数：5
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