Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

Abstract This paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound reciprocally convex quadratic terms, we provide an improved combination inequality (RCCI), which includes some existing ones as its particular cases. We construct eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, MJPs. Based well-designed event-triggered control scheme, several novel stability criteria underlying systems by employing new RCCI other analytical techniques. Finally, present two numerical examples show validity our methods.