Noise‐tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

Abstract The solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate.