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具有随机发生不确定性和模态相关时变时滞的中立型马尔可夫跳跃混沌神经网络的非脆弱鲁棒同步。

Non-fragile robust synchronization for Markovian jumping chaotic neural networks of neutral-type with randomly occurring uncertainties and mode-dependent time-varying delays.

机构信息

Department of Mathematics, Bharathiar University, Coimbatore 641046, Tamilnadu, India.

Department of Mathematics, Bharathiar University, Coimbatore 641046, Tamilnadu, India.

出版信息

ISA Trans. 2014 Nov;53(6):1760-70. doi: 10.1016/j.isatra.2014.09.022. Epub 2014 Oct 18.

Abstract

This paper deals with the problem of robust synchronization for uncertain chaotic neutral-type Markovian jumping neural networks with randomly occurring uncertainties and randomly occurring control gain fluctuations. Then, a sufficient condition is proposed for the existence of non-fragile output controller in terms of linear matrix inequalities (LMIs). Uncertainty terms are separately taken into consideration. This network involves both mode dependent discrete and mode dependent distributed time-varying delays. Based on the Lyapunov-Krasovskii functional (LKF) with new triple integral terms, convex combination technique and free-weighting matrices method, delay-dependent sufficient conditions for the solvability of these problems are established in terms of LMIs. Furthermore, the problem of non-fragile robust synchronization is reduced to the optimization problem involving LMIs, and the detailed algorithm for solving the restricted LMIs is given. Numerical examples are provided to show the effectiveness of the proposed theoretical results.

摘要

本文针对具有随机发生不确定性和随机发生控制增益波动的不确定中立型马尔可夫跳变神经网络的鲁棒同步问题进行了研究。然后,基于具有新三重积分项的李雅普诺夫-克拉索夫斯基泛函(LKF)、凸组合技术和自由加权矩阵方法,提出了一种基于线性矩阵不等式(LMI)的非脆弱输出控制器存在的充分条件。该网络同时包含模式相关离散时滞和模式相关分布时滞。最后,将非脆弱鲁棒同步问题转化为涉及 LMI 的优化问题,并给出了求解受限 LMI 的详细算法。数值算例验证了所提出理论结果的有效性。

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