title: Novel adaptive strategies for synchronization of linearly coupled neural networks with reaction-diffusion terms.
abstract: In this paper, two types of linearly coupled neural networks with reaction-diffusion terms are proposed. We respectively investigate the adaptive synchronization of these two types of complex network models. With local information of node dynamics, some novel adaptive strategies to tune the coupling strengths among network nodes are designed. By constructing appropriate Lyapunov functionals and using inequality techniques, several sufficient conditions are given for reaching synchronization by using the designed adaptive laws. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results.
authors.forename: Jin-Liang
authors.initials: JL
authors.lastname: Wang
authors.forename: Huai-Ning
authors.initials: HN
authors.lastname: Wu
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004058:Diffusion; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013997:Time Factors