title: Global dissipativity of memristor-based neutral type inertial neural networks.
abstract: The problem of global dissipativity for memristor-based inertial networks with time-varying delay of neutral type is investigated in this paper. Based on a proper variable substitution, the inertial system is transformed into a conventional system. Some sufficient criteria are established to ascertain the global dissipativity for the aforementioned inertial neural networks by employing analytical techniques and Lyapunov method. Meanwhile, the globally exponentially attractive sets and positive invariant sets are also presented here. Finally, numerical examples and simulations are given out to corroborate the effectiveness of obtained results.
authors.affiliation: School of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210996, Jiangsu, China; School of Mathematics and Statistics, and Key Laboratory for Nonlinear Science and System Structure, Chongqing Three Gorges University, Wanzhou 404100, Chongqing, China.
authors.forename: Zhengwen
authors.initials: Z
authors.lastname: Tu
authors.affiliation: School of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210996, Jiangsu, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
authors.affiliation: Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Alsaedi
authors.affiliation: Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors.forename: Fuad
authors.initials: F
authors.lastname: Alsaadi
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer
keywords: Dissipativity; Inertial neural networks; Memristor; Neutral