File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/s12591-010-0075-x
- Scopus: eid_2-s2.0-80053383188
- WOS: WOS:000217279900008
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Robust stability for interval stochastic neural networks with time-varying discrete and distributed delays
Title | Robust stability for interval stochastic neural networks with time-varying discrete and distributed delays |
---|---|
Authors | |
Keywords | Interval systems Neural networks Robust stability Stochastic systems Time-varying delays |
Issue Date | 2011 |
Publisher | Springer (India) Private Ltd. The Journal's web site is located at http://www.springer.com/math/journal/12591 |
Citation | Differential Equations and Dynamical Systems, 2011, v. 19 n. 1-2, p. 97-118 How to Cite? |
Abstract | This paper investigates the problem of robust stability for a class of stochastic interval neural networks with discrete and distributed time-varying delays. The discrete delays are assumed to be varying within a given interval, while the parameter uncertainties are assumed to be bounded in some given compact sets. Based on the Itô differential formula and stochastic stability theory, delay-range-dependent criteria for stochastic interval neural networks with time-varying delays are derived to guarantee robust global asymptotic stability in mean square. In this paper, when estimating the upper bound of the derivative of Lyapunov functionals, we are concerned with better handling of terms related to the discrete and distributed delays and establishing less conservative results. These robust stability conditions are presented in terms of linear matrix inequalities and can be efficiently solved via standard numerical software. An important feature of the results proposed in this paper is that the stability conditions are dependent on the upper and lower bounds of the discrete delays. Numerical examples are given to illustrate the effectiveness of the proposed method. © 2010 Foundation for Scientific Research and Technological Innovation. |
Persistent Identifier | http://hdl.handle.net/10722/139424 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.305 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, H | en_US |
dc.contributor.author | Cheung, KC | en_US |
dc.contributor.author | Lam, J | en_US |
dc.contributor.author | Gao, H | en_US |
dc.date.accessioned | 2011-09-23T05:49:19Z | - |
dc.date.available | 2011-09-23T05:49:19Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.citation | Differential Equations and Dynamical Systems, 2011, v. 19 n. 1-2, p. 97-118 | en_US |
dc.identifier.issn | 0971-3514 | - |
dc.identifier.uri | http://hdl.handle.net/10722/139424 | - |
dc.description.abstract | This paper investigates the problem of robust stability for a class of stochastic interval neural networks with discrete and distributed time-varying delays. The discrete delays are assumed to be varying within a given interval, while the parameter uncertainties are assumed to be bounded in some given compact sets. Based on the Itô differential formula and stochastic stability theory, delay-range-dependent criteria for stochastic interval neural networks with time-varying delays are derived to guarantee robust global asymptotic stability in mean square. In this paper, when estimating the upper bound of the derivative of Lyapunov functionals, we are concerned with better handling of terms related to the discrete and distributed delays and establishing less conservative results. These robust stability conditions are presented in terms of linear matrix inequalities and can be efficiently solved via standard numerical software. An important feature of the results proposed in this paper is that the stability conditions are dependent on the upper and lower bounds of the discrete delays. Numerical examples are given to illustrate the effectiveness of the proposed method. © 2010 Foundation for Scientific Research and Technological Innovation. | - |
dc.language | eng | en_US |
dc.publisher | Springer (India) Private Ltd. The Journal's web site is located at http://www.springer.com/math/journal/12591 | - |
dc.relation.ispartof | Differential Equations and Dynamical Systems | en_US |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.subject | Interval systems | - |
dc.subject | Neural networks | - |
dc.subject | Robust stability | - |
dc.subject | Stochastic systems | - |
dc.subject | Time-varying delays | - |
dc.title | Robust stability for interval stochastic neural networks with time-varying discrete and distributed delays | en_US |
dc.type | Article | en_US |
dc.identifier.email | Cheung, KC: kccheung@hkucc.hku.hk | en_US |
dc.identifier.email | Lam, J: james.lam@hku.hk | en_US |
dc.identifier.authority | Cheung, KC=rp01322 | en_US |
dc.identifier.authority | Lam, J=rp00133 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s12591-010-0075-x | - |
dc.identifier.scopus | eid_2-s2.0-80053383188 | - |
dc.identifier.hkuros | 196459 | en_US |
dc.identifier.volume | 19 | en_US |
dc.identifier.issue | 1-2 | en_US |
dc.identifier.spage | 97 | en_US |
dc.identifier.epage | 118 | en_US |
dc.identifier.eissn | 0974-6870 | - |
dc.identifier.isi | WOS:000217279900008 | - |
dc.publisher.place | India | - |
dc.identifier.issnl | 0971-3514 | - |