Delay-distribution-dependent H∞ state estimation for delayed neural networks with (x, v)-dependent noises and fading channels. (December 2016)

• Record Type: Journal Article
• Authors: Sheng, Li; Wang, Zidong; Tian, Engang; Alsaadi, Fuad E.
• Is Part Of: Neural networks. Volume 84(2016)
• Page Start: 102
• Page End: 112
• Publication Date: 2016-12
• Subjects: Delayed neural networks -- H∞ state estimation -- Delay-distribution-dependent condition -- Random delay -- (x, v)-dependent noises -- Fading channels; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.08.013
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper deals with the H ∞ state estimation problem for a class of discrete-time neural networks with stochastic delays subject to state- and disturbance-dependent noises (also called ( x, v ) -dependent noises) and fading channels. The time-varying stochastic delay takes values on certain intervals with known probability distributions. The system measurement is transmitted through fading channels described by the Rice fading model. The aim of the addressed problem is to design a state estimator such that the estimation performance is guaranteed in the mean-square sense against admissible stochastic time-delays, stochastic noises as well as stochastic fading signals. By employing the stochastic analysis approach combined with the Kronecker product, several delay-distribution-dependent conditions are derived to ensure that the error dynamics of the neuron states is stochastically stable with prescribed H ∞ performance. Finally, a numerical example is provided to illustrate the effectiveness of the obtained results.