Computing Time-Varying ML-Weighted Pseudoinverse by the Zhang Neural Networks
Sanzheng Qiao, Yimin Wei, Xuxin Zhang
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.