PARAMETER-ESTIMATION OF STATE-SPACE MODELS BY RECURRENT NEURAL NETWORKS

被引：13
|
作者：
RAOL, JR
机构：
[1] Natl Aerospace Lab, Bangalore
来源：
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS | 1995年 / 142卷 / 02期
关键词：
STATE SPACE MODELS; RECURRENT NEURAL NETWORKS; HOPFIELD NEURAL NETWORK;
D O I：
10.1049/ip-cta:19951733
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Four variants of recurrent neural networks (RNNs) are studied. The similarities and contradistinction of these formulations are brought out from the view point of their applicability to parameter estimation in dynamic systems. The trajectory matching algorithms are also given. A recursive information processing scheme within the structure of Hopfield neural network for parameter estimation is presented. Numerical simulation results for nonrecursive and recursive schemes are given.
引用
收藏
页码：114 / 118
页数：5
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