In this paper, the global exponential stability and periodicity are investigated for delayed neural network models with continuous coefficients and piecewise constant delay of generalized type. The sufficient condition for the existence and uniqueness of periodic solutions of the model is established by applying Banach’s fixed point theorem and the successive approximations method. By constructing suitable differential inequalities with generalized piecewise constant delay, some sufficient conditions for the global exponential stability of the model are obtained. Typical numerical examples with simulations are utilized to illustrate the validity and improvement in less conservatism of the theoretical results. This paper ends with a brief conclusion.

中文翻译：

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广义分段常数时滞神经网络模型的全局指数周期和稳定性

本文研究了具有连续系数和广义常数分段时滞的时滞神经网络模型的全局指数稳定性和周期性。应用Banach不动点定理和逐次逼近方法，建立了模型周期解存在和唯一的充分条件。通过构造具有广义分段常数延迟的适当微分不等式，为模型的全局指数稳定性提供了一些充分的条件。典型的数值例子与仿真被用来说明理论结果的保守性的有效性和改进。本文以一个简短的结论结束。