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A Novel Robust Stability Framework for Impulsive Discrete-Time Switched Stochastic Hopfield Neural Networks
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DOI：10.1002/acs.70115.png)
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Abstract
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This paper investigates robust exponential stability and control for a class of discrete-time switched stochastic Hopfield neural networks with impulsive effects. First, a unified analysis framework is established for discrete-time switched stochastic impulsive systems. By combining Lyapunov theory with the average dwell-time approach, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to guarantee robust exponential stability and prescribed disturbance attenuation. Then, these results are extended to switched stochastic Hopfield neural networks with impulsive effects by incorporating sector conditions on the activation functions, leading to tractable state-feedback controller design criteria. The developed method explicitly captures the coupled influence of switching, stochastic disturbances, and impulsive behavior. Finally, the effectiveness of the proposed method is verified through numerical examples of photovoltaic energy storage charging stations and networked unmanned vehicle systems.
Keywords:
average dwell time
H∞$$ {H}_{\infty } $$ control
Hopfield neural networks
LMIs
robust exponential stability
switched systems
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