An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components

作者：Nagamani, Gnaneswaran^*; Radhika, Thirunavukkarasu; Zhu, Quanxin

来源：IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(12): 3018-3031.

DOI：10.1109/TNNLS.2016.2608360

• Additive time-varying delays
• dissipativity
• GFL
• linear matrix inequality (LMI)
• Lyapunov method

In this paper, we investigate the dissipativity and passivity of Markovian jump stochastic neural networks involving two additive time-varying delays. Using a Lyapunov-Krasovskii functional with triple and quadruple integral terms, we obtain delay-dependent passivity and dissipativity criteria for the system. Using a generalized Finsler lemma (GFL), a set of slack variables with special structure are introduced to reduce design conservatism. The dissipativity and passivity criteria depend on the upper bounds of the discrete time-varying delay and its derivative are given in terms of linear matrix inequalities, which can be efficiently solved through the standard numerical software. Finally, our illustrative examples show that the proposed method performs well and is successful in problems where existing methods fail.

• 出版日期2017-12
• 单位南京师范大学