School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China; Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.
ISA Trans. 2017 Jul;69:102-121. doi: 10.1016/j.isatra.2017.04.001. Epub 2017 Apr 20.
This paper studies the problem of passivity analysis for neural networks with two different Markovian jumping parameters and mixed time delays utilizing some integral inequalities. The integral inequalities produce sharper bounds than what the Jensen's inequality produces, consequently, better results are obtained. The Markovian jumping parameters in connection weight matrices and discrete delay are assumed to be different in the system model. By constructing a new appropriate Lyapunov-Krasovskii functional (LKF), some sufficient conditions are established which guarantee the passivity of the proposed model. Numerical examples are given to show the less conservatism and effectiveness of the proposed method.
本文利用一些积分不等式研究了两个不同马尔可夫跳跃参数和混合时滞神经网络的被动性分析问题。积分不等式产生的界比 Jensen 不等式更严格,因此可以得到更好的结果。在系统模型中,连接权重矩阵和离散延迟中的马尔可夫跳跃参数被假定为不同。通过构造一个新的适当的李雅普诺夫-克拉索夫斯基函数(LKF),建立了一些充分条件,保证了所提出模型的被动性。数值例子表明了所提出方法的较小保守性和有效性。