Robust stability of discrete-time LPD neural networks with time-varying delay
This paper presents a new approach to the robust stability of discrete-time LPD neural networks with time-varying delay and with normed bounded uncertainties as well as polytopic type uncertainties. Based on Lyapunov stability theory and the S-procedure, we derive robust stability criteria in terms...
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th-cmuir.6653943832-492382018-08-16T02:12:54Z Robust stability of discrete-time LPD neural networks with time-varying delay S. Udpin P. Niamsup Mathematics This paper presents a new approach to the robust stability of discrete-time LPD neural networks with time-varying delay and with normed bounded uncertainties as well as polytopic type uncertainties. Based on Lyapunov stability theory and the S-procedure, we derive robust stability criteria in terms of linear matrix inequalities (LMI) which are solvable by several available algorithms. We show that some of the existing results on robust stability of neural networks are corollaries of main results of this paper. Numerical examples are given to illustrate the effectiveness of our theoretical results. © 2008 Elsevier B.V. All rights reserved. 2018-08-16T02:12:54Z 2018-08-16T02:12:54Z 2009-11-01 Journal 10075704 2-s2.0-67349193191 10.1016/j.cnsns.2008.08.018 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67349193191&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49238 |
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Mathematics |
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Mathematics S. Udpin P. Niamsup Robust stability of discrete-time LPD neural networks with time-varying delay |
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This paper presents a new approach to the robust stability of discrete-time LPD neural networks with time-varying delay and with normed bounded uncertainties as well as polytopic type uncertainties. Based on Lyapunov stability theory and the S-procedure, we derive robust stability criteria in terms of linear matrix inequalities (LMI) which are solvable by several available algorithms. We show that some of the existing results on robust stability of neural networks are corollaries of main results of this paper. Numerical examples are given to illustrate the effectiveness of our theoretical results. © 2008 Elsevier B.V. All rights reserved. |
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Journal |
| author |
S. Udpin P. Niamsup |
| author_facet |
S. Udpin P. Niamsup |
| author_sort |
S. Udpin |
| title |
Robust stability of discrete-time LPD neural networks with time-varying delay |
| title_short |
Robust stability of discrete-time LPD neural networks with time-varying delay |
| title_full |
Robust stability of discrete-time LPD neural networks with time-varying delay |
| title_fullStr |
Robust stability of discrete-time LPD neural networks with time-varying delay |
| title_full_unstemmed |
Robust stability of discrete-time LPD neural networks with time-varying delay |
| title_sort |
robust stability of discrete-time lpd neural networks with time-varying delay |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67349193191&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49238 |
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