Exponential Synchronization of Switched Neural Networks With Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules

Yuting Cao, Shiqin Wang, Shiping Wen

Abstract: This paper is devoted to the exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-based rules. At first, by introducing an indicator function, the switched neural networks are transformed into neural networks with general form. Then, sufficient conditions are deduced to achieve exponential synchronization for drive-response systems by two different types of event-triggering rules, i.e., static and dynamic event-triggering rules. Meanwhile, we can ensure that the Zeno phenomenon does not occur by proving that the time interval between two successive trigger events has a positive lower bound. Finally, two illustrative examples are elaborated to substantiate the theoretical results.