title: Stochastic sampled-data control for state estimation of time-varying delayed neural networks.
abstract: This study examines the state estimation problem for neural networks with a time-varying delay. Unlike other studies, the sampled-data with stochastic sampling is used to design the state estimator using a novel approach that divides the bounding of the activation function into two subintervals. To fully use the sawtooth structure characteristics of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. The desired estimator gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Finally, the proposed method is applied to two numerical examples to show the effectiveness of our result.
authors.affiliation: Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, 214-1 Dae-Dong, Kyongsan 712-749, Republic of Korea.
authors.forename: Tae H
authors.initials: TH
authors.lastname: Lee
authors.forename: Ju H
authors.initials: JH
authors.lastname: Park
authors.forename: O M
authors.initials: OM
authors.lastname: Kwon
authors.forename: S M
authors.initials: SM
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000379:methods; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors
keywords: Neural networks; Sampled-data; State estimator; Stochastic sampling; Time-varying delay