Sowmiya C, Raja R, Cao Jinde, Rajchakit G, Alsaedi Ahmed
Department of Mathematics, Alagappa University, Karaikudi, 630 004 India.
Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi, 630 004 India.
Adv Differ Equ. 2017;2017(1):318. doi: 10.1186/s13662-017-1378-9. Epub 2017 Oct 10.
This paper is concerned with the problem of enhanced results on robust finite-time passivity for uncertain discrete-time Markovian jumping BAM delayed neural networks with leakage delay. By implementing a proper Lyapunov-Krasovskii functional candidate, the reciprocally convex combination method together with linear matrix inequality technique, several sufficient conditions are derived for varying the passivity of discrete-time BAM neural networks. An important feature presented in our paper is that we utilize the reciprocally convex combination lemma in the main section and the relevance of that lemma arises from the derivation of stability by using Jensen's inequality. Further, the zero inequalities help to propose the sufficient conditions for finite-time boundedness and passivity for uncertainties. Finally, the enhancement of the feasible region of the proposed criteria is shown via numerical examples with simulation to illustrate the applicability and usefulness of the proposed method.
本文关注具有泄漏延迟的不确定离散时间马尔可夫跳跃BAM延迟神经网络在鲁棒有限时间无源方面的结果增强问题。通过构造一个合适的Lyapunov-Krasovskii泛函候选函数，结合倒数凸组合方法和线性矩阵不等式技术，推导出了几个用于改变离散时间BAM神经网络无源特性的充分条件。本文呈现的一个重要特征是，我们在主要部分使用了倒数凸组合引理，并且该引理的相关性源于通过使用Jensen不等式推导稳定性。此外，零不等式有助于为不确定性的有限时间有界性和无源特性提出充分条件。最后，通过数值例子和仿真展示了所提准则可行域的增强，以说明所提方法的适用性和有效性。
