title: Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks.
abstract: The present paper is devoted to investigating the global dissipativity for inertial neural networks with time-varying delays and parameter uncertainties. By virtue of a suitable substitution, the original system is transformed to the first order differential system. By means of matrix measure, generalized Halanay inequality, and matrix-norm inequality, several sufficient criteria for the global dissipativity of the addressed neural networks are proposed. Meanwhile, the specific estimations of positive invariant sets and globally attractive sets are obtained. Finally, two examples are provided to validate our theoretical results.
authors.affiliation: Department 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: Department 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: Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Department of Mathematics, Quaid-I-Azam University, Islamabad 44000, Pakistan.
authors.forename: Tasawar
authors.initials: T
authors.lastname: Hayat
mesh_terms: D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty
keywords: Dissipativity; Inertial neural networks; Matrix measure; Uncertainty