title: Synchronization of stochastic competitive neural networks with different timescales and reaction-diffusion terms.
abstract: We propose a feedback controller for the synchronization of stochastic competitive neural networks with different timescales and reaction-diffusion terms. By constructing a proper Lyapunov-Krasovskii functional, as well as employing stochastic analysis theory, the LaShall-type invariance principle for stochastic differential delay equations, and a linear matrix inequality (LMI) technique, a feedback controller is designed to achieve the asymptotical synchronization of coupled stochastic competitive neural networks. A simulation example is given to show the effectiveness of the theoretical results.
authors.affiliation: School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 611731, China cdycshi@163.com.
authors.forename: Yanchao
authors.initials: Y
authors.lastname: Shi
authors.forename: Peiyong
authors.initials: P
authors.lastname: Zhu
mesh_terms: D004058:Diffusion; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D013995:Time