Abstract
This paper deals with the problem of state estimation for discretestochastic recurrent neural network with interval time-delays. The activationfunctions are assumed to be globally Lipschitz continuous. Attention is focusedon the design of a state estimator which ensures the global stability of theestimation error dynamics. A delay-dependent condition with dependence on theupper and lower bounds of the delays is given in terms of a linear matrixinequality (LMI) to solve the neuron state estimation problem. When this LMI isfeasible, the expression of a desired state estimator is also presented. Inaddition, slack matrices are introduced to reduce the conservatism of thecondition. A numerical example is provided to demonstrate the applicability ofthe proposed approach. ICIC International
| Original language | English |
|---|---|
| Pages (from-to) | 465-470 |
| Number of pages | 6 |
| Journal | ICIC Express Letters |
| Volume | 3 |
| Issue number | 3 |
| Publication status | Published - 2009 Sep 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Control and Systems Engineering
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A delay-dependent approach to design state estimator for discretestochastic recurrent neural network with interval time-varying delays. / Liao, Chin Wen; Lu, Chien Yu; Zheng, Kai Yuan; Ting, Chien Chung.
In: ICIC Express Letters, Vol. 3, No. 3, 01.09.2009, p. 465-470.Research output: Contribution to journal › Article
TY - JOUR
T1 - A delay-dependent approach to design state estimator for discretestochastic recurrent neural network with interval time-varying delays
AU - Liao, Chin Wen
AU - Lu, Chien Yu
AU - Zheng, Kai Yuan
AU - Ting, Chien Chung
PY - 2009/9/1
Y1 - 2009/9/1
N2 - This paper deals with the problem of state estimation for discretestochastic recurrent neural network with interval time-delays. The activationfunctions are assumed to be globally Lipschitz continuous. Attention is focusedon the design of a state estimator which ensures the global stability of theestimation error dynamics. A delay-dependent condition with dependence on theupper and lower bounds of the delays is given in terms of a linear matrixinequality (LMI) to solve the neuron state estimation problem. When this LMI isfeasible, the expression of a desired state estimator is also presented. Inaddition, slack matrices are introduced to reduce the conservatism of thecondition. A numerical example is provided to demonstrate the applicability ofthe proposed approach. ICIC International
AB - This paper deals with the problem of state estimation for discretestochastic recurrent neural network with interval time-delays. The activationfunctions are assumed to be globally Lipschitz continuous. Attention is focusedon the design of a state estimator which ensures the global stability of theestimation error dynamics. A delay-dependent condition with dependence on theupper and lower bounds of the delays is given in terms of a linear matrixinequality (LMI) to solve the neuron state estimation problem. When this LMI isfeasible, the expression of a desired state estimator is also presented. Inaddition, slack matrices are introduced to reduce the conservatism of thecondition. A numerical example is provided to demonstrate the applicability ofthe proposed approach. ICIC International
UR - http://www.scopus.com/inward/record.url?scp=77953894929&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953894929&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:77953894929
VL - 3
SP - 465
EP - 470
JO - ICIC Express Letters
JF - ICIC Express Letters
SN - 1881-803X
IS - 3
ER -