ABSTRACT

This work investigates a class of inertial Cohen-Grossberg neural networks incorporating distributed delays using a direct mathematical analysis approach. First, by exploiting Lyapunov functional method, one can verify that the solutions of the addressed system are exponentially attractive to each other. Second, with the help of differential inequality techniques, the existence and global exponential stability of the anti-periodic solution are gained. Lastly, a simulation example is provided to illustrate the theoretical findings.

中文翻译：

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涉及分布式延迟的惯性 Cohen-Grossberg 神经网络的反周期性

摘要

这项工作使用直接数学分析方法研究了一类包含分布式延迟的惯性 Cohen-Grossberg 神经网络。首先，通过利用 Lyapunov 函数方法，可以验证所寻系统的解决方案彼此之间呈指数级吸引。其次，借助微分不等式技术，得到了反周期解的存在性和全局指数稳定性。最后，提供了一个仿真例子来说明理论发现。