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Improved Results on H∞ State Estimation of Static Neural Networks with Time Delay
Joint Authors
Zhong, Shouming
Wen, Bin
Li, Hui
Source
Journal of Control Science and Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-12
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
This paper studies the problem of H∞ state estimation for a class of delayed static neural networks.
The purpose of the problem is to design a delay-dependent state estimator such that the dynamics of the error system is globally exponentially stable and a prescribed H∞ performance is guaranteed.
Some improved delay-dependent conditions are established by constructing augmented Lyapunov-Krasovskii functionals (LKFs).
The desired estimator gain matrix can be characterized in terms of the solution to LMIs (linear matrix inequalities).
Numerical examples are provided to illustrate the effectiveness of the proposed method compared with some existing results.
American Psychological Association (APA)
Wen, Bin& Li, Hui& Zhong, Shouming. 2016. Improved Results on H∞ State Estimation of Static Neural Networks with Time Delay. Journal of Control Science and Engineering،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1107872
Modern Language Association (MLA)
Wen, Bin…[et al.]. Improved Results on H∞ State Estimation of Static Neural Networks with Time Delay. Journal of Control Science and Engineering No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1107872
American Medical Association (AMA)
Wen, Bin& Li, Hui& Zhong, Shouming. Improved Results on H∞ State Estimation of Static Neural Networks with Time Delay. Journal of Control Science and Engineering. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1107872
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1107872