Stability of Cohen-Grossberg Neural Networks with Nonnegative Periodic solutions

Tianping Chen, Yanchun Bai
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引用次数: 4

Abstract

In this paper, we discuss nonnegative periodic solutions for generalized Cohen-Grossberg neural networks. Without assuming strict positivity and boundedness of the amplification functions, the dynamics of periodic Cohen-Grossberg neural networks are studied. By applying a direct method, sufficient conditions guaranteeing the existence and global asymptotic stability of nonnegative periodic solution are derived. Also the criterion does not depend on the assumption for amplification functions being upper and low bounded or the external inputs.
具有非负周期解的Cohen-Grossberg神经网络的稳定性
本文讨论了广义Cohen-Grossberg神经网络的非负周期解。在不严格假设放大函数的正性和有界性的情况下,研究了周期Cohen-Grossberg神经网络的动力学。利用直接方法,得到了保证非负周期解存在性和全局渐近稳定的充分条件。此外,该准则不依赖于放大函数是上界和下界的假设或外部输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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