PMID- 28364660
OWN - NLM
STAT- MEDLINE
DCOM- 20170523
LR  - 20191210
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 89
DP  - 2017 May
TI  - H(infinity) state estimation for discrete-time neural networks with distributed delays 
      and randomly occurring uncertainties through Fading channels.
PG  - 61-73
LID - S0893-6080(16)30193-9 [pii]
LID - 10.1016/j.neunet.2016.12.004 [doi]
AB  - In this paper, the H(infinity) state estimation problem is investigated for a class of 
      uncertain discrete-time neural networks subject to infinitely distributed delays 
      and fading channels. Randomly occurring uncertainties (ROUs) are introduced to 
      reflect the random nature of the network condition fluctuations, and the channel 
      fading phenomenon is considered to account for the possibly unreliable network 
      medium on which the measurement signal is transmitted. A set of 
      Bernoulli-distributed white sequences are employed to govern the ROUs and the 
      L-th Rice fading model is utilized where channel coefficients are mutually 
      independent random variables with certain probability density function on [0,1]. 
      We aim to design a state estimator such that the dynamics of the estimation error 
      is asymptotically stable while satisfying the prescribed H(infinity) performance 
      constraint. By adopting the Lyapunov-Krasovskii functional and the stochastic 
      analysis theory, sufficient conditions are established to ensure the existence of 
      the desired state estimators and the explicit expression of such estimators is 
      acquired. A simulation example is provided to verify the usefulness of the 
      proposed approach.
CI  - Copyright (c) 2016 Elsevier Ltd. All rights reserved.
FAU - Hou, Nan
AU  - Hou N
AD  - College of Electrical and Information Engineering, Northeast Petroleum 
      University, Daqing 163318, China.
FAU - Dong, Hongli
AU  - Dong H
AD  - College of Electrical and Information Engineering, Northeast Petroleum 
      University, Daqing 163318, China. Electronic address: shiningdhl@vip.126.com.
FAU - Wang, Zidong
AU  - Wang Z
AD  - Department of Computer Science, Brunel University London, Uxbridge, Middlesex, 
      UB8 3PH, United Kingdom. Electronic address: Zidong.Wang@brunel.ac.uk.
FAU - Ren, Weijian
AU  - Ren W
AD  - College of Electrical and Information Engineering, Northeast Petroleum 
      University, Daqing 163318, China.
FAU - Alsaadi, Fuad E
AU  - Alsaadi FE
AD  - Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20170213
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - Algorithms
MH  - Computer Simulation
MH  - Models, Theoretical
MH  - *Neural Networks, Computer
MH  - Random Allocation
MH  - Time Factors
MH  - *Uncertainty
OTO - NOTNLM
OT  - Channel fadings
OT  - Infinitely distributed delays
OT  - Randomly occurring uncertainties
OT  - Recurrent neural networks
OT  - State estimation
EDAT- 2017/04/02 06:00
MHDA- 2017/05/24 06:00
CRDT- 2017/04/02 06:00
PHST- 2016/06/12 00:00 [received]
PHST- 2016/10/10 00:00 [revised]
PHST- 2016/12/09 00:00 [accepted]
PHST- 2017/04/02 06:00 [pubmed]
PHST- 2017/05/24 06:00 [medline]
PHST- 2017/04/02 06:00 [entrez]
AID - S0893-6080(16)30193-9 [pii]
AID - 10.1016/j.neunet.2016.12.004 [doi]
PST - ppublish
SO  - Neural Netw. 2017 May;89:61-73. doi: 10.1016/j.neunet.2016.12.004. Epub 2017 Feb 
      13.