New Event Based <i>H</i> _∞ State Estimation for Discrete-Time Recurrent Delayed Semi-Markov Jump Neural Networks Via a Novel Summation Inequality

Abstract This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is introduced to find measurement output with a specific triggering condition so as lower burden of data communication. A novel summation inequality established existence asymptotic stability error system. The problem addressed here construct an H ∞ that guarantees inequality, characterized by event-triggered transmission. By Lyapunov functional technique, explicit expressions gain are established. Finally, two examples exploited numerically illustrate usefulness new methodology.