Adaptive quantitative exponential synchronization in multiplex Cohen-Grossberg neural networks under deception attacks

In this article, a secure exponential synchronization problem is studied for multiplex Cohen-Grossberg neural networks under stochastic deception attacks. In order to resist the malicious attack from attackers modifying the data in transmission module under a certain probability, an attack resistant controller, which has the ability to automatically adjust its own parameters according to external attacks, is designed for each Cohen-Grossberg neural subnet. An exponential adaptive quantitative controlling algorithm is proposed to synchronize Cohen-Grossberg neural network state, and a sufficient criterion is established to realize the synchronization error tends to zero under malicious attacks. Moreover, synchronization mode we study is the synchronization among Cohen-Grossberg neural subnets in multiplex networks. An example is presented to testify the validity of proposed theoretical framework. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.