An anti-interference dynamic integral neural network for solving the time-varying linear matrix equation with periodic noises.

In order to solve a time-varying linear matrix equation with periodic noises, an anti-interference dynamic integral neural network (AI-DINN) is proposed. Based on an indefinite unbounded vector/matrix-type error function, the proposed AI-DINN includes an integral structure, a recursive structure, and an adjustment module. It has the excellent ability to overcome the interference of periodic noises. This paper theoretically proves the convergence and robustness of the proposed AI-DINN for solving the time-varying linear matrix equation with the interference of periodic noises. Computer simulation results verify that the proposed AI-DINN method based on different activation functions can achieve convergence within limited time with the interferences of different periodic noises. In addition, the proposed AI-DINN with different activation functions has its own advantages with the interference of different types of periodic noises. Furthermore, comparative simulation experiments verify that the proposed AI-DINN has better convergence and anti-interference performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]