Finite-horizon ${H_{\infty }}$ State Estimation for Discrete-Time Memristive Neural Networks with Time-Varying Delays and Fading Channels: A Dynamic Event-Triggered Approach

In this paper, we consider the
${H_{\infty }}$
finite-horizon state estimation issue for a class of discrete-time memristive neural networks (DMNNs) with time-varying delays and multiple fading channels. A set of correlated random variables are used to study the correlated fading channels. Specifically, the channel coefficients are assumed to be scalar white noise processes satisfying certain probability distribution laws. At the same time, in order to reduce unnecessary resource consumption in the communication channel between the sensor and remote estimator, a dynamic event-triggered transmission strategy is introduced. Our goal is to construct a set of dynamic event-triggered
${H_{\infty }}$
estimators to ensure that the DMNNs reaches the specified interference attenuation level over a finite time-horizon. With the combination of the Lyapunov-Krasovskii function and stochastic analysis method, sufficient conditions are obtained for the expected estimator. Then, the estimator gains are calculated by solving a set of convex optimization problems. Finally, an example is given to verify the effectiveness and usefulness of the developed state estimation scheme.