A RECURRENT NEURAL NETWORK FOR REAL-TIME MATRIX-INVERSION

被引：114
|
作者：
WANG, J
机构：
[1] Department of Industrial Technology University of North Dakota, Grand Forks
来源：
APPLIED MATHEMATICS AND COMPUTATION | 1993年 / 55卷 / 01期
关键词：
D O I：
10.1016/0096-3003(93)90007-2
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A recurrent neural network for computing inverse matrices in real-time is proposed. The proposed recurrent neural network consists of n independent subnetworks where n is the order of the matrix. The proposed recurrent neural network is proven to be asymptotically stable and capable of computing large-scale nonsingular inverse matrices in real-time. An op-amp based analog neural network is discussed. The operating characteristics of the op-amp based analog neural network is also demonstrated via an illustrative example.
引用
收藏
页码：89 / 100
页数：12
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