时变时滞正神经网络的全局指数稳定性。
On global exponential stability of positive neural networks with time-varying delay.
机构信息
Department of Mathematics, Hanoi National University of Education, 136 Xuan Thuy Road, Hanoi, Viet Nam.
出版信息
Neural Netw. 2017 Mar;87:22-26. doi: 10.1016/j.neunet.2016.11.004. Epub 2016 Dec 1.
This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positive neural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable condition is derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. The effectiveness of the obtained results is illustrated by a numerical example.
本文提出了一个新的结果，即在存在有界时变时滞的情况下，正神经网络的正平衡点的存在性、唯一性和全局指数稳定性。基于一些新的比较技术，推导出了一个可测试的条件，以确保系统的所有状态轨迹都指数收敛到唯一的正平衡点。通过一个数值例子说明了所得到的结果的有效性。
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