Computing Time-Varying ML-Weighted Pseudoinverse by the Zhang Neural Networks. (25th October 2020)

• Record Type: Journal Article
• Authors: Qiao, Sanzheng; Wei, Yimin; Zhang, Xuxin
• Is Part Of: Numerical functional analysis and optimization. Volume 41:Number 14(2020)
• Page Start: 1672
• Page End: 1693
• Publication Date: 2020-10-25
• Subjects: ML-weighted pseudoinverse -- time-varying matrix -- Zhang neural network; Functional analysis -- Periodicals; Numerical analysis -- Periodicals; Mathematical optimization -- Periodicals; Numerical Analysis, Computer-Assisted; 515.705
• DOI: 10.1080/01630563.2020.1740887
• Languages: English
• ISSNs: 0163-0563

Abstract

Abstract: The Zhang neural network (ZNN), a recurrent neural network, proposed in 2001, is particularly effective in solving time-varying problems. It has shown high efficiency and excellent performance in various applications. The weighted pseudoinverse is a useful tool in solving and analyzing the constrained least-squares problems. In this paper, we propose a ZNN model for computing the weighted pseudoinverse of a time-varying matrix. We show that our model converges globally and exponentially to the solution and our system is robust at the presence of small errors. A Matlab Simulink implementation of our model is presented. Our convergence analysis is verified by our experiments on testing matrices. A comparison study shows that our model has superior performance over the conventional gradient-based neural networks.