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

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Sep 19, 2016

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.

Authors

  • Li Sheng
    Department of Drug Metabolism, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Xiannongtan Street, Beijing 100050, China; Beijing Key Laboratory of Non-Clinical Drug Metabolism and PK/PD Study, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Xiannongtan Street, Beijing 100050, China; Beijing Key Laboratory of Active Substances Discovery and Drug Ability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Xiannongtan Street, Beijing 100050, China; State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Xiannongtan Street, Beijing 100050, China. Electronic address: shengli@imm.ac.cn.
  • Zidong Wang
    Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK; Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: zidong.wang@brunel.ac.uk.
  • Engang Tian
    School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China.
  • Fuad E Alsaadi
    Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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