School of Sciences, Southwest Petroleum University, Chengdu, Sichuan 610500, China.
ISA Trans. 2014 Jul;53(4):1000-5. doi: 10.1016/j.isatra.2014.05.010. Epub 2014 Jun 3.
In this paper, the problem of delay-dependent asymptotic stability analysis for neural networks with time-varying delays is considered. A new class of Lyapunov functional is proposed by considering the information of neuron activation functions adequately. By using the delay-partitioning method and the reciprocally convex technique, some less conservative stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the effectiveness of the derived method.
本文研究了时变时滞神经网络的时滞相关渐近稳定性分析问题。通过充分考虑神经元激活函数的信息，提出了一类新的李雅普诺夫泛函。利用时滞分区法和互凸技术，基于线性矩阵不等式（LMI）得到了一些更保守的稳定性判据。最后，通过两个数值算例验证了所提出方法的有效性。
