Nonlinearly Activated IEZNN Model for Solving Time-Varying Sylvester Equation

Abstract

Zeroing neural network (ZNN) is an effective method to calculate time-varying problems. However, the ZNN and its extensions separately addressed the robustness and the convergence. To simultaneously promote the robustness and finite-time convergence, a nonlinearly activated integration-enhanced ZNN (NIEZNN) model based on a coalescent activation function (C-AF) has been designed for solving the time-varying Sylvester equation in various noise situations. The C-AF with an optimized structure is convenient for simulations and calculations, which promotes NIEZNN accelerates convergence speed without remarkable efficiency loss. The robustness and the finite-time convergence of the NIEZNN model have been proved in theoretical analyses. Further more, the upper bounds of convergence time of the NIEZNN model and the noise-attached NIEZNN model have been deduced in theory. At last, numerical comparative results and the application to mobile manipulator have validated the efficiency and superiority of the NIEZNN model based on the designed coalescent activation function.