文献类型：期刊文献

英文题名：New results on passivity analysis of memristive neural networks with time-varying delays and reaction-diffusion term

作者：Wei, Hongzhi[1];Chen, Chunrong[1];Tu, Zhengwen[2];Li, Ning[3]

第一作者：Wei, Hongzhi

通讯作者：Wei, HZ[1]

机构：[1]Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China;[2]Chongqing Three Gorges Univ, Sch Math & Stat, Wanzhou 404100, Peoples R China;[3]Henan Univ Econ & Law, Coll Math & Informat Sci, Zhengzhou 450046, Henan, Peoples R China

第一机构：Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China

通讯机构：[1]corresponding author), Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China.

年份：2018

卷号：275

起止页码：2080-2092

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20174704439346);Scopus(收录号：2-s2.0-85034619781);WOS:【SCI-EXPANDED(收录号:WOS:000418370200194)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities (Grant no. 106112015CDJXY100002). The Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant no. KJ1501002), National Natural Science Foundation of China under Grant 61603125 and Key Program of Higher Education of He'nan Province under Grant 17A120001.

语种：英文

外文关键词：Memristor; Passivity Reaction-diffusion term; Poincare inequality; Linear matrix inequality (LMI)

摘要：In this paper, we put in effort to inspect the passivity and exponential passivity problem of memristive neural networks with time-varying delays and reaction-diffusion term. Based on the basis of generalized Lyapunov approach, Poincare inequality, Schur complement Lemma, free-weighting matrix approach as well as some inequality techniques, the main conclusions of this paper are derived in the form of linear matrix inequality (LMI). What is noteworthy is that the obtained sufficient criteria rely on the reaction-diffusion term, which implies that the reaction-diffusion term can effect the passivity of the given system. Finally, two numerical examples are emerged to check the practicability of the derived passivity conditions. (C) 2017 Elsevier B.V. All rights reserved.