Existence, learning, and replication of periodic motions in recurrent neural networks

被引：60
作者：
Ruiz, A
Owens, DH
Townley, S
机构：
[1] Univ Exeter, Ctr Syst & Control Engn, Exeter EX4 4QF, Devon, England
[2] Univ Exeter, Dept Math, Exeter EX4 4QE, Devon, England
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1998年 / 9卷 / 04期
基金：
英国工程与自然科学研究理事会;
关键词：
Hopf bifurcation; learning systems; neural networks; nonlinear dynamics;
D O I：
10.1109/72.701178
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A class of recurrent neural networks is shown to possess a stable limit cycle. A gradient type algorithm is used to modify the parameters of the network so that it learns and replicates autonomously a time varying periodic signal. The results are applied to controlling the repetitive motion of a two-link robot manipulator.
引用
收藏
页码：651 / 661
页数：11
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