Zeroing neural networks for solving discrete periodic Riccati matrix equations

In this article, a novel reduced inversion zeroing neural network (RIZNN) is first proposed to solve the discrete periodic Riccati matrix equation (DPRE). To enhance the convergence and robustness performance of the RIZNN model, a nonlinear activation function, called Sine-Exponential activation function (SEAF), is constructed by combining a hyperbolic sine function with an exponential function. The convergence properties of the RIZNN model activated by SEAF are also proven under different noise conditions. Finally, simulation results are employed to illustrate the superiority of the RIZNN model activated by SEAF.