Dissipative State Estimation for Delayed Discrete-Time Memristive Neural Networks Under Dynamic Event-Triggered Mechanisms and Deception Attacks

In this paper, the state estimation problem for delayed discrete-time memristive neural networks subject to deception attacks is concerned. The main purpose is to design a state estimator such that the error dynamics of the state estimation is stochastically stable and strict dissipative. The randomness of deception attacks is described via a set of Bernoulli distributed white sequences with known conditional probabilities. In addition, in order to reduce unnecessary resource consumption in the communication channels, a dynamic event-triggering mechanism is introduced. By using the Lyapunov-Krasovskii functional and stochastic analysis method, sufficient conditions for stochastic stability and strict dissipation of the error system are acquired. On this basis, a co-design scheme of estimator parameters and the event-triggered matrix is proposed. Finally, the numerical simulation shows the effectiveness of the proposed state estimation scheme.