Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151172
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dc.contributor.authorPan, Jinfengen_US
dc.contributor.authorFeng, Jun-een_US
dc.contributor.authorMeng, Minen_US
dc.date.accessioned2021-06-17T02:50:32Z-
dc.date.available2021-06-17T02:50:32Z-
dc.date.issued2019-
dc.identifier.citationPan, J., Feng, J. & Meng, M. (2019). Steady-state analysis of probabilistic Boolean networks. Journal of the Franklin Institute, 356(5), 2994-3009. https://dx.doi.org/10.1016/j.jfranklin.2019.01.039en_US
dc.identifier.issn0016-0032en_US
dc.identifier.urihttps://hdl.handle.net/10356/151172-
dc.description.abstractThis paper investigates steady-state distributions of probabilistic Boolean networks via cascading aggregation. Under this approach, the problem is converted to computing least square solutions to several corresponding equations. Two necessary and sufficient conditions for the existence of the steady-state distributions for probabilistic Boolean networks are given firstly. Secondly, an algorithm for finding the steady-state distributions of probabilistic probabilistic Boolean networks is given. Finally, a numerical example is given to show the effectiveness of the proposed method.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of the Franklin Instituteen_US
dc.rights© 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSteady-state analysis of probabilistic Boolean networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.jfranklin.2019.01.039-
dc.identifier.scopus2-s2.0-85061155208-
dc.identifier.issue5en_US
dc.identifier.volume356en_US
dc.identifier.spage2994en_US
dc.identifier.epage3009en_US
dc.subject.keywordsGenetic Networksen_US
dc.subject.keywordsControllabilityen_US
dc.description.acknowledgementThis work was supported by National Natural Science Foundation under Grants 61773371 and 61877036, and China Postdoctoral Science Foundation under Grant 2016M-602143.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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