title: Global point dissipativity of neural networks with mixed time-varying delays.
abstract: By employing the Lyapunov method and some inequality techniques, the global point dissipativity is studied for neural networks with both discrete time-varying delays and distributed time-varying delays. Simple sufficient conditions are given for checking the global point dissipativity of neural networks with mixed time-varying delays. The proposed linear matrix inequality approach is computationally efficient as it can be solved numerically using standard commercial software. Illustrated examples are given to show the usefulness of the results in comparison with some existing results.
authors.affiliation: Department of Mathematics, Southeast University, Nanjing 210096, People's Republic of China. jdcao@seu.edu.cn
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
authors.forename: Kun
authors.initials: K
authors.lastname: Yuan
authors.forename: Daniel W C
authors.initials: DW
authors.lastname: Ho
authors.forename: James
authors.initials: J
authors.lastname: Lam
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors