title: Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling.
abstract: In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results.
authors.affiliation: Department of Radiology, JinLing Hospital of Nanjing, China; Jinling Hospital-Fudan University Computational Translational Medicine Center, Centre for Computational Systems Biology, and School of Mathematical Sciences, Fudan University, China; School of Mathematics, Fudan University, 200433, Shanghai, China. Electronic address: wenlian@fudan.edu.cn.
authors.forename: Wenlian
authors.initials: W
authors.lastname: Lu
authors.affiliation: School of Mathematics, Fudan University, 200433, Shanghai, China. Electronic address: 12110180051@fudan.edu.cn.
authors.forename: Ren
authors.initials: R
authors.lastname: Zheng
authors.affiliation: School of Mathematics, Fudan University, 200433, Shanghai, China; School of Computer Sciences and School of Mathematics Sciences, Fudan University, 200433, Shanghai, China. Electronic address: tchen@fudan.edu.cn.
authors.forename: Tianping
authors.initials: T
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors
keywords: Centralized principle; Data sampling; Decentralized principle; Outer-synchronization; Recurrent neural networks