Abstract
This paper investigates the exponential stability of stochastic inertial neural networks (SINNs) with generalised piecewise constant argument. By applying variable substitution, the second-order system is transformed into a first-order system. Subsequently, several sufficient conditions are introduced to ensure the existence of a unique solution. Due to the presence of generalised piecewise constant argument, calculus theory is applied to estimate the dynamic effects of network states in current time and deviation function. The global mean-square exponential stability of SINNs with generalised piecewise constant argument is established using the Lyapunov function method. Finally, two numerical examples are provided to verify the validity of derived theoretical results.
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