Shao Hanyong
School of Electrical and Information Automation, Qufu Normal University, Rizhao, Shandong 276826 China.
IEEE Trans Neural Netw. 2008 Sep;19(9):1647-51. doi: 10.1109/TNN.2008.2001265.
This brief is concerned with the stability for static neural networks with time-varying delays. Delay-independent conditions are proposed to ensure the asymptotic stability of the neural network. The delay-independent conditions are less conservative than existing ones. To further reduce the conservatism, delay-dependent conditions are also derived, which can be applied to fast time-varying delays. Expressed in linear matrix inequalities, both delay-independent and delay-dependent stability conditions can be checked using the recently developed algorithms. Examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed result.
本简报关注具有时变延迟的静态神经网络的稳定性。提出了与延迟无关的条件以确保神经网络的渐近稳定性。这些与延迟无关的条件比现有条件保守性更低。为了进一步降低保守性，还推导了与延迟相关的条件，其可应用于快速时变延迟。用线性矩阵不等式表示，与延迟无关和与延迟相关的稳定性条件都可以使用最近开发的算法进行检验。给出了示例以说明所提结果的有效性和降低的保守性。
