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|e rakwb
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|a eng
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| 100 |
1 |
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|a Yuan, Kun
|e verfasserin
|4 aut
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| 245 |
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|a Robust stability of switched Cohen-Grossberg Neural networks with mixed time-varying delays
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|c 2006
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
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|2 rdamedia
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|2 rdacarrier
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|a Date Completed 05.03.2007
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|a Date Revised 10.11.2019
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a By combining Cohen-Grossberg neural networks with an arbitrary switching rule, the mathematical model of a class of switched Cohen-Grossberg neural networks with mixed time-varying delays is established. Moreover, robust stability for such switched Cohen-Grossberg neural networks is analyzed based on a Lyapunov approach and linear matrix inequality (LMI) technique. Simple sufficient conditions are given to guarantee the switched Cohen-Grossberg neural networks to be globally asymptotically stable for all admissible parametric uncertainties. The proposed LMI-based results are computationally efficient as they can be solved numerically using standard commercial software. An example is given to illustrate the usefulness of the results
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Cao, Jinde
|e verfasserin
|4 aut
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|a Li, Han-Xiong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
|d 1997
|g 36(2006), 6 vom: 15. Dez., Seite 1356-63
|w (DE-627)NLM098252887
|x 1083-4419
|7 nnns
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| 773 |
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|g volume:36
|g year:2006
|g number:6
|g day:15
|g month:12
|g pages:1356-63
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|d 36
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