DocumentCode
1290314
Title
Observer Design for Switched Recurrent Neural Networks: An Average Dwell Time Approach
Author
Lian, Jie ; Feng, Zhi ; Shi, Peng
Author_Institution
Fac. of Electron. Inf. & Electr. Eng., Dalian Univ. of Technol., Dalian, China
Volume
22
Issue
10
fYear
2011
Firstpage
1547
Lastpage
1556
Abstract
This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
Keywords
asymptotic stability; computability; delays; linear matrix inequalities; observers; recurrent neural nets; time-varying systems; average dwell time approach; delay-dependent sufficient condition; error dynamic system; error-state decay estimate; free-weighting matrix technique; global exponential stability; linear matrix inequalities; observer design; solvability; stability analysis; switched recurrent neural network; time-varying delay; Biological neural networks; Delay; Neurons; Observers; Recurrent neural networks; Stability analysis; Switches; Average dwell time method; exponential stability; observer design; switched neural networks; time-varying delay; Algorithms; Artificial Intelligence; Humans; Linear Models; Neural Networks (Computer); Observation; Software Design; Software Validation; Time Factors;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2011.2162111
Filename
5975222
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