Anti-Synchronization of Discrete-Time Fuzzy Memristive Neural Networks via Impulse Sampled-Data Communication
Fen Liu, Wei Meng, Renquan Lu
Abstract
This work is concerned with the anti-synchronization (A-S) of drive-response (D-R) memristive neural networks (MNNs) based on fuzzy rules. A novel impulsive sampled-data communication mechanism is proposed by considering information security of the MNNs, in which the random response delay of sensors caused by the impulse signal is also investigated. As the state of MNNs cannot be outputted accurately and transmitted persistently, the state observers of the D-R MNNs are established, which is beneficial to design the A-S controller. By analyzing the stability of the augmented error system (AES) based on the fuzzy-based Lyapunov-Krasovskii functional (FLKF), sufficient conditions of the A-S between D-R MNNs are derived. An illustrative example is given to verify the effectiveness of the proposed A-S strategies.