Improved delay-dependent stability criteria for neural networks with discrete and distributed time-varying delays using a delay-partitioning approach

被引：0
作者：
Kaibo Shi
Hong Zhu
Shouming Zhong
Yong Zeng
Yuping Zhang
机构：
[1] University of Electronic Science and Technology of China,School of Automation Engineering
[2] University of Electronic Science and Technology of China,School of Mathematical Sciences
[3] University of Electronic Science and Technology of China,Key Laboratory for Neuroinformation of Ministry of Education
来源：
Nonlinear Dynamics | 2015年 / 79卷
关键词：
Neural networks; Stability analysis; Delay decomposition approach; Terms-quadratic convex combination; Mixed time-varying delays; Linear matrix inequalities (LMIs);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper is focused on the problem of delay-dependent stability criteria for neural networks (NNs) with discrete and distributed time-varying delays. Firstly, by constructing a newly augmented Lyapunov–Krasovskii functionals with multiple integral terms, less conservative stability criteria are formulated in terms of linear matrix inequalities. Secondly, some improved delay-dependent stability results are obtained by dividing the discrete and distributed delays into multiple nonuniformly subintervals and using a novel activation function condition. Besides, by employing the idea of second-order convex combination and the property of quadratic convex function which has been not used in the previous papers of NNs with mixed time-varying delays, further improved delay-dependent stability conditions are proposed. Finally, two numerical examples are given to verify the effectiveness and superiority of our proposed main results.
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页码：575 / 592
页数：17
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