کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
411804 | 679589 | 2015 | 12 صفحه PDF | دانلود رایگان |
This study is concerned with the problem of stability analysis of neutral-type neural networks with mixed random time-varying delays. Firstly, by using a novel and resultful mathematical approach and considering the sufficient information of neuron activation functions, improved delay-dependent stability results are formulated in terms of linear matrix inequalities (LMIs). Secondly, in order to obtain less conservative delay-dependent stability criteria, an augmented novel Lyapunov–Krasovskii functional (LKF) that contains triple and quadruple-integral terms is constructed. Moreover, our derivation makes full use of the idea of second-order convex combination and the property of quadratic convex function, which plays a key role in reducing further the conservatism of conditions. Finally, four numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results.
Journal: Neurocomputing - Volume 168, 30 November 2015, Pages 896–907