Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

被引：0
|
作者：
Qiuhong Xiang
Bolin Liao
Lin Xiao
Long Lin
Shuai Li
机构：
[1] Jishou University,College of Mathematics and Statistics
[2] Jishou University,College of Information Science and Engineering
[3] Lanzhou University,School of Information Science and Engineering
[4] Hong Kong Polytechnic University,Department of Computing
来源：
Soft Computing | 2019年 / 23卷
关键词：
Discrete time; Noise tolerant; Dynamic matrix pseudoinverse; Theoretical analysis; Numerical examples;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.
引用
收藏
页码：755 / 766
页数：11
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