New delay-dependent stability criteria for neutral-type neural networks with mixed random time-varying delays ?
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 LyapunovKrasovskii 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.
neutral-type neural networks mixed random time-varying delays stability analysis linear matrix inequality terms-quadratic convex combination