A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

In this paper, a zeroing neural network model with properties of non-convex activation and noise suppressing (NANSZNN) is proposed for finding the square root of a time-varying matrix. In comparison with the existing zeroing neural network models, the proposed NANSZNN model relieves the limit of convex constraint and makes a breakthrough in noise-suppressing. Furthermore, theoretical analyses and strict proofs are provided in detail for the global convergence and noise-suppressing performance of the proposed NANSZNN model under the circumstances of various noises by using Lyaponov stability theory. Finally, numerical experiments and comparative analyses are offered to further illustrate the availability, effectiveness, and robustness against different noises of the proposed NANSZNN model.