Event-triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism. This paper addresses the two most common issues that exist in large-scale networks: gain variations in the estimator design and lack of network resources. Based on the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to address this issue, considering both the additive and multiplicative structured gain variations. Through the Lyapunov-Krasovskii functional method and linear matrix inequality technique, sufficient conditions are derived to guarantee the existence of the proposed state estimator by employing the generalized free-weighting matrix inequality and improved reciprocally convex inequality. Moreover, the obtained linear matrix inequalities provide an explicit expression of the estimator gain matrix and triggering matrix. Eventually, two numerical examples are provided to authenticate the proposed findings.

Automated summary

This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism, addressing the common issues of gain variations and lack of network resources in large-scale networks. By utilizing the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to consider both additive and multiplicative structured gain variations. Sufficient conditions are derived using the Lyapunov-Krasovskii functional method and linear matrix inequality technique, providing explicit expressions of the estimator gain matrix and triggering matrix.