Mean-square input-to-state stability for stochastic complex-valued neural networks with neutral delay

In this paper, the issue of input-to-state exponential stability (ISES) for stochastic complex-valued neural networks with neutral delay (SCVNNs) and discrete delay is considered. Without separating the SCVNNs into two real-valued systems, two criteria expressed through linear matrix inequality (LMI) to pledge ISES of the considered SCVNNs are derived based on Ito formula in complex-valued field, Lyapunov-Krasovskii functional approach as well as some relevant inequality skills. Two examples are furnished to verify the raised results. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper considers the issue of input-to-state exponential stability (ISES) for stochastic complex-valued neural networks with neutral delay (SCVNNs) and discrete delay. Two criteria expressed through linear matrix inequality (LMI) are derived based on Ito formula in complex-valued field and Lyapunov-Krasovskii functional approach to ensure the stability of the networks. Two examples are provided to verify the results.