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Published on: November 12, 2019
Synchronization of state-dependent switched neural networks via non-exponential lyapunov functions with stochastic
Lulu Li1, Lvqin Wang1, Lei Wang2
1School of Mathematics, Hefei University of Technology, Hefei 230009, China.
Abstract:
This paper studies the synchronization of switched neural networks (SNNs) under stochastic delayed impulses (SDI), utilizing the state-dependent switching (SDS) mechanism based on the information from node dynamics. Unlike prior works, we construct a generalized non-exponential Lyapunov function for stability analysis. We extend the differential inequality with non-exponential function to the case with impulsive delay and discuss the positive effect of impulsive delay on the system's stability. By combining the Lyapunov theory, and SDS mechanisms, we derive a synchronization criterion of SNNs with SDS mechanisms under SDI. Numerical examples are presented to demonstrate the effectiveness of our results.
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