L2-L∞ state estimation for continuous stochastic delayed neural networks via memory event-triggering strategy

In this paper, we study the memory event-triggering $ L_2 $ L2- $ L_\infty $ L∞ state estimation for a type of continuous stochastic neural networks (NNs) subject to time-varying delays. The information of some recent released packets is made use in the proposed triggering conditions to schedule the data propagation, thereby reducing communication frequency and saving energy. By taking into account network-induced complexities (i.e. transmission delays and random disturbances), we first formulate the evolutions of estimation error in an augmented form, and then propose the conditions under with the design goals could be met. By using certain novel Lyapunov–Krasovskii (L–K) functionals in combination with stochastic analysis technique, sufficient conditions have been provided for the existence of desired estimator, guaranteeing both the globally asymptotically mean-square stability and the prescribed $ L_2 $ L2- $ L_\infty $ L∞ performance simultaneously. Moreover, the estimator gains are obtained by virtue of certain convex optimisation algorithms. Finally, we use an illustrative example to verify the obtained theoretical algorithm.