Uniform Stability of Complex-Valued Neural Networks of Fractional Order With Linear Impulses and Fixed Time Delays

As a generation of the real-valued neural network (RVNN), complex-valued neural network (CVNN) is based on the complex-valued (CV) parameters and variables. The fractional-order (FO) CVNN with linear impulses and fixed time delays is discussed. By using the sign function, the Banach fixed point theorem, and two classes of activation functions, some criteria of uniform stability for the solution and existence and uniqueness for equilibrium solution are derived. Finally, three experimental simulations are presented to illustrate the correctness and effectiveness of the obtained results.

Automated summary

This paper discusses the complex-valued neural network based on CV parameters and variables, focusing on the fractional-order CVNN with linear impulses and fixed time delays. Criteria for uniform stability and existence and uniqueness of equilibrium solutions were derived using the sign function, Banach fixed point theorem, and two classes of activation functions. Three experimental simulations were presented to illustrate the correctness and effectiveness of the obtained results.