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Article: Estimating Stable Uncertainty Sets for Genetic Regulatory Networks with Guaranteed Disturbance Attenuation

TitleEstimating Stable Uncertainty Sets for Genetic Regulatory Networks with Guaranteed Disturbance Attenuation
Authors
KeywordsUncertain Genetic Regulatory Networks
Stability Regions
Disturbance Attenuation
Semidefinite Programming
Issue Date2014
PublisherAmerican V-King Scientific Publishing, Ltd. The Journal's web site is located at http://www.ijcet.org/Home.aspx
Citation
Journal of Control Engineering and Technology, 2014, v. 4 n. 1, p. 22-28 How to Cite?
AbstractIt is well-known that models of genetic regulatory networks (GRNs) are unavoidably affected by uncertainties. This paper addresses the problem of estimating stable uncertainty sets of uncertain GRNs with guaranteed disturbance attenuation. Specifically, the GRNs are assumed to be affected by disturbances in the form of Wiener processes, and by uncertainties in the form of a parameter vector that determines the coefficients of the model via given functions. It is shown that estimates of the sought stable uncertainty sets can be obtained through a recursive strategy based on parameter-dependent Lyapunov functions and convex optimization. Some examples with fictitious and real biological models illustrate the use of the proposed strategy.
Persistent Identifierhttp://hdl.handle.net/10722/199076
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLi, Jen_US
dc.contributor.authorChesi, Gen_US
dc.date.accessioned2014-07-22T01:02:44Z-
dc.date.available2014-07-22T01:02:44Z-
dc.date.issued2014en_US
dc.identifier.citationJournal of Control Engineering and Technology, 2014, v. 4 n. 1, p. 22-28en_US
dc.identifier.issn2223-2036-
dc.identifier.urihttp://hdl.handle.net/10722/199076-
dc.description.abstractIt is well-known that models of genetic regulatory networks (GRNs) are unavoidably affected by uncertainties. This paper addresses the problem of estimating stable uncertainty sets of uncertain GRNs with guaranteed disturbance attenuation. Specifically, the GRNs are assumed to be affected by disturbances in the form of Wiener processes, and by uncertainties in the form of a parameter vector that determines the coefficients of the model via given functions. It is shown that estimates of the sought stable uncertainty sets can be obtained through a recursive strategy based on parameter-dependent Lyapunov functions and convex optimization. Some examples with fictitious and real biological models illustrate the use of the proposed strategy.-
dc.languageengen_US
dc.publisherAmerican V-King Scientific Publishing, Ltd. The Journal's web site is located at http://www.ijcet.org/Home.aspx-
dc.relation.ispartofJournal of Control Engineering and Technologyen_US
dc.subjectUncertain Genetic Regulatory Networks-
dc.subjectStability Regions-
dc.subjectDisturbance Attenuation-
dc.subjectSemidefinite Programming-
dc.titleEstimating Stable Uncertainty Sets for Genetic Regulatory Networks with Guaranteed Disturbance Attenuationen_US
dc.typeArticleen_US
dc.identifier.emailChesi, G: chesi@eee.hku.hken_US
dc.identifier.authorityChesi, G=rp00100en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.hkuros230385en_US
dc.identifier.volume4en_US
dc.identifier.issue1-
dc.identifier.spage22en_US
dc.identifier.epage28en_US
dc.publisher.placeUnited States-

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