Exponential stability of state-dependent impulsive Hopfield neural networks with beating phenomena.

Dai, Zhong; Liu, Shutang · Neural Netw · 2025

basic_science · Level V

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Abstract

This paper establishes the exponential stability for state-dependent impulsive Hopfield neural networks with beating phenomena. Based on the assumptions, arbitrary trajectory of state-dependent impulsive systems (SDISs) intersects each impulsive surface only a finite number of times, and we extend the B-equivalence method to reformulate SDISs as fixed-time impulsive systems (FTISs). Two sets of sufficient conditions are derived to ensure the exponential stability of SDISs. Additionally, the stability properties of reduced FTISs are discussed. Numerical examples validate the theoretical results.

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