Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control

This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov?Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results. ? 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the stability and stabilizability of complex-valued memristive neural networks with random time-varying delays via non-fragile sampled-data control. A non-fragile sampled-data controller is designed for CVMNNs, taking into account the influence of gain fluctuations. The new stability and stabilizability criteria are derived based on the average values of the maximum and minimum of the memristive connection weights, different from existing results.