Stability Analysis of Neutral Stochastic Pantograph System with Time-dependent Switching and its Applications in Neural Networks

This paper investigates the stability of a highly non-linear neutral stochastic pantograph system with time-dependent switching (NSPSTS) and its applications in neural networks. Firstly, the concept of switching density is introduced to describe the switching frequency of the nonlinear switching system. Subsequently, under the novel switching mechanism, by utilizing the Lyapunov function technique and stochastic theory, the existence and uniqueness of the solution of NSPSTS are discussed, and one criterion on pth moment $\psi-$stability is derived. Moreover, the presented findings are applied to neural networks (NNs). At last, an example is presented to demonstrate the validity of the theoretical findings.