Robust Controllability of Boolean Control Networks via Dynamic Programming
This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucia...
Uloženo v:
| Vydáno v: | IEEE transaction on neural networks and learning systems Ročník 36; číslo 9; s. 17448 - 17461 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.09.2025
|
| Témata: | |
| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucial concept characterizing robust reachability between two arbitrary states. We develop a finite-termination dynamic programming algorithm to calculate the optimal time matrix exactly and efficiently, with a rigorously certified iteration count. Sufficient and necessary conditions for robust controllability are then established based on the optimal time matrix. Furthermore, for any pair of reachable states, we construct time-optimal state feedback control laws to steer the system from the initial state to the target state, regardless of disturbances. Finally, extensive numerical experiments with biological networks validate the effectiveness of the proposed approach, showing significant improvements in computational efficiency. Additionally, we introduce a Q -learning-based algorithm and compare its performance, highlighting the advantages of our dynamic programming approach in terms of both efficiency and solution quality. |
|---|---|
| AbstractList | This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucial concept characterizing robust reachability between two arbitrary states. We develop a finite-termination dynamic programming algorithm to calculate the optimal time matrix exactly and efficiently, with a rigorously certified iteration count. Sufficient and necessary conditions for robust controllability are then established based on the optimal time matrix. Furthermore, for any pair of reachable states, we construct time-optimal state feedback control laws to steer the system from the initial state to the target state, regardless of disturbances. Finally, extensive numerical experiments with biological networks validate the effectiveness of the proposed approach, showing significant improvements in computational efficiency. Additionally, we introduce a Q -learning-based algorithm and compare its performance, highlighting the advantages of our dynamic programming approach in terms of both efficiency and solution quality. This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucial concept characterizing robust reachability between two arbitrary states. We develop a finite-termination dynamic programming algorithm to calculate the optimal time matrix exactly and efficiently, with a rigorously certified iteration count. Sufficient and necessary conditions for robust controllability are then established based on the optimal time matrix. Furthermore, for any pair of reachable states, we construct time-optimal state feedback control laws to steer the system from the initial state to the target state, regardless of disturbances. Finally, extensive numerical experiments with biological networks validate the effectiveness of the proposed approach, showing significant improvements in computational efficiency. Additionally, we introduce a Q-learning-based algorithm and compare its performance, highlighting the advantages of our dynamic programming approach in terms of both efficiency and solution quality.This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic disturbances. By applying Bellman's optimality principle, we derive the recurrence relation for computing the optimal time matrix, a crucial concept characterizing robust reachability between two arbitrary states. We develop a finite-termination dynamic programming algorithm to calculate the optimal time matrix exactly and efficiently, with a rigorously certified iteration count. Sufficient and necessary conditions for robust controllability are then established based on the optimal time matrix. Furthermore, for any pair of reachable states, we construct time-optimal state feedback control laws to steer the system from the initial state to the target state, regardless of disturbances. Finally, extensive numerical experiments with biological networks validate the effectiveness of the proposed approach, showing significant improvements in computational efficiency. Additionally, we introduce a Q-learning-based algorithm and compare its performance, highlighting the advantages of our dynamic programming approach in terms of both efficiency and solution quality. |
| Author | Wu, Jianliang Gao, Yiming Xiang, Cheng Feng, Jun-e Li, Yakun Gao, Shuhua |
| Author_xml | – sequence: 1 givenname: Yakun orcidid: 0000-0001-5635-5554 surname: Li fullname: Li, Yakun email: liyakunmath@mail.sdu.edu.cn organization: School of Mathematics, Shandong University, Jinan, China – sequence: 2 givenname: Shuhua orcidid: 0000-0003-3208-9620 surname: Gao fullname: Gao, Shuhua email: shuhuagao@sdu.edu.cn organization: School of Control Science and Engineering, Shandong University, Jinan, China – sequence: 3 givenname: Yiming surname: Gao fullname: Gao, Yiming email: GAOY0053@e.ntu.edu.sg organization: School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore – sequence: 4 givenname: Jianliang orcidid: 0000-0003-0422-6915 surname: Wu fullname: Wu, Jianliang email: jlwu@sdu.edu.cn organization: School of Mathematics, Shandong University, Jinan, China – sequence: 5 givenname: Jun-e orcidid: 0000-0003-3881-3042 surname: Feng fullname: Feng, Jun-e email: fengjune@sdu.edu.cn organization: School of Mathematics, Shandong University, Jinan, China – sequence: 6 givenname: Cheng orcidid: 0000-0002-1229-6860 surname: Xiang fullname: Xiang, Cheng email: elexc@nus.edu.sg organization: Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40388283$$D View this record in MEDLINE/PubMed |
| BookMark | eNpFkMtOwzAQRS0EoqX0BxBCWbJJ8SOxnSWUt6qCoEjsLMeZVIEkLnYC6t-T0gfejOU5dzQ-R2i_tjUgdELwiBCcXMym08nriGIaj1gcJxSLPdSnhNOQMin3d3fx3kND7z9wdziOeZQcol6EO4ZK1kePLzZtfROMbd04W5Y6LcqiWQY2D66sLUHX21YwhebHuk8ffBc6uF7WuipM8Ozs3OmqKur5MTrIdelhuKkD9HZ7Mxvfh5Onu4fx5SQ0VIgm5FrmccwFFilAlAtgQmLJMgpplKaCGtCSGp3y7iXPEoYjxhLCjDGQSaYxG6Dz9dyFs18t-EZVhTfQ7V6Dbb1iFMeSCMxph55t0DatIFMLV1TaLdX2_x1A14Bx1nsH-Q4hWK08qz_PauVZbTx3odN1qACA_wDpBAtO2C8ykHlS |
| CODEN | ITNNAL |
| Cites_doi | 10.1007/s11538-017-0306-1 10.1109/LRA.2022.3194326 10.1109/TAC.2018.2880411 10.1109/TNNLS.2024.3380247 10.1371/journal.pone.0046798 10.1016/j.jfranklin.2021.07.010 10.1016/j.automatica.2019.108621 10.1109/TNNLS.2022.3192563 10.1109/TCYB.2020.3003552 10.1109/TAC.2017.2702008 10.1109/TCSII.2023.3309334 10.1016/0022-5193(73)90247-6 10.1109/TCYB.2021.3092374 10.1109/TSMC.2022.3195196 10.1016/S0022-5193(03)00035-3 10.1038/s41467-021-25533-3 10.1109/TCYB.2020.3022430 10.1109/TAC.2018.2830642 10.1016/j.amc.2022.126992 10.1155/2023/6690805 10.1007/s11432-019-9904-6 10.1371/journal.pcbi.1000936 10.1109/LRA.2022.3179420 10.1016/j.automatica.2014.12.018 10.1109/CDC.1992.371340 10.1109/tnnls.2024.3430906 10.1016/j.asoc.2024.111687 10.1371/journal.pcbi.1002267 10.1016/0022-5193(69)90015-0 10.1038/nrg2918 10.1093/bib/bbae286 10.1016/j.automatica.2009.03.006 10.1126/sciadv.abf8124 10.1016/j.ifacol.2022.07.605 10.1016/j.ins.2023.01.017 10.1016/j.jtbi.2006.09.023 10.1109/TAC.2013.2294821 10.1109/TAC.2010.2043294 10.1155/2019/1813594 10.1371/journal.pcbi.1004193 10.1016/j.automatica.2018.06.030 10.1016/j.neunet.2024.106266 10.1109/TNNLS.2022.3164909 10.1016/j.automatica.2017.01.032 10.1016/j.automatica.2017.07.013 10.1038/s41592-019-0690-6 10.1038/s41598-022-20979-x |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7X8 |
| DOI | 10.1109/TNNLS.2025.3559207 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2162-2388 |
| EndPage | 17461 |
| ExternalDocumentID | 40388283 10_1109_TNNLS_2025_3559207 11006761 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 62303277; 11971270; 11631014 funderid: 10.13039/501100001809 – fundername: Natural Science Fund of Shandong Province grantid: 2023HWYQ-003; ZR2023QF048 – fundername: Young Experts of Taishan Scholar Project grantid: tsqn202306067 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM 7X8 |
| ID | FETCH-LOGICAL-c277t-6a8f556707bee4f7e378083d2eb4bb72cea82cab6d2efd930433913ccced83a03 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001494234100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Sat Nov 01 14:26:55 EDT 2025 Sat Sep 06 11:16:05 EDT 2025 Sat Nov 29 07:34:59 EST 2025 Wed Sep 17 06:32:16 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c277t-6a8f556707bee4f7e378083d2eb4bb72cea82cab6d2efd930433913ccced83a03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-3208-9620 0000-0003-0422-6915 0000-0001-5635-5554 0000-0002-1229-6860 0000-0003-3881-3042 |
| PMID | 40388283 |
| PQID | 3205817062 |
| PQPubID | 23479 |
| PageCount | 14 |
| ParticipantIDs | crossref_primary_10_1109_TNNLS_2025_3559207 pubmed_primary_40388283 proquest_miscellaneous_3205817062 ieee_primary_11006761 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-09-01 |
| PublicationDateYYYYMMDD | 2025-09-01 |
| PublicationDate_xml | – month: 09 year: 2025 text: 2025-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transaction on neural networks and learning systems |
| PublicationTitleAbbrev | TNNLS |
| PublicationTitleAlternate | IEEE Trans Neural Netw Learn Syst |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref46 ref45 ref48 ref47 ref42 Bertsekas (ref29) 2012; 4 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref30 Sutton (ref31) 2018 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 |
| References_xml | – volume-title: Reinforcement Learning: An Introduction year: 2018 ident: ref31 – ident: ref42 doi: 10.1007/s11538-017-0306-1 – ident: ref49 doi: 10.1109/LRA.2022.3194326 – ident: ref16 doi: 10.1109/TAC.2018.2880411 – ident: ref17 doi: 10.1109/TNNLS.2024.3380247 – ident: ref39 doi: 10.1371/journal.pone.0046798 – ident: ref13 doi: 10.1016/j.jfranklin.2021.07.010 – ident: ref12 doi: 10.1016/j.automatica.2019.108621 – ident: ref20 doi: 10.1109/TNNLS.2022.3192563 – ident: ref36 doi: 10.1109/TCYB.2020.3003552 – ident: ref10 doi: 10.1109/TAC.2017.2702008 – ident: ref30 doi: 10.1109/TCSII.2023.3309334 – ident: ref2 doi: 10.1016/0022-5193(73)90247-6 – ident: ref24 doi: 10.1109/TCYB.2021.3092374 – ident: ref22 doi: 10.1109/TSMC.2022.3195196 – ident: ref18 doi: 10.1016/S0022-5193(03)00035-3 – ident: ref3 doi: 10.1038/s41467-021-25533-3 – ident: ref25 doi: 10.1109/TCYB.2020.3022430 – ident: ref34 doi: 10.1109/TAC.2018.2830642 – volume: 4 year: 2012 ident: ref29 publication-title: Dynamic Programming and Optimal Control: Volume I – ident: ref27 doi: 10.1016/j.amc.2022.126992 – ident: ref6 doi: 10.1155/2023/6690805 – ident: ref37 doi: 10.1007/s11432-019-9904-6 – ident: ref41 doi: 10.1371/journal.pcbi.1000936 – ident: ref46 doi: 10.1109/LRA.2022.3179420 – ident: ref38 doi: 10.1016/j.automatica.2014.12.018 – ident: ref32 doi: 10.1109/CDC.1992.371340 – ident: ref5 doi: 10.1109/tnnls.2024.3430906 – ident: ref47 doi: 10.1016/j.asoc.2024.111687 – ident: ref43 doi: 10.1371/journal.pcbi.1002267 – ident: ref1 doi: 10.1016/0022-5193(69)90015-0 – ident: ref8 doi: 10.1038/nrg2918 – ident: ref45 doi: 10.1093/bib/bbae286 – ident: ref9 doi: 10.1016/j.automatica.2009.03.006 – ident: ref4 doi: 10.1126/sciadv.abf8124 – ident: ref48 doi: 10.1016/j.ifacol.2022.07.605 – ident: ref23 doi: 10.1016/j.ins.2023.01.017 – ident: ref40 doi: 10.1016/j.jtbi.2006.09.023 – ident: ref28 doi: 10.1109/TAC.2013.2294821 – ident: ref11 doi: 10.1109/TAC.2010.2043294 – ident: ref21 doi: 10.1155/2019/1813594 – ident: ref19 doi: 10.1371/journal.pcbi.1004193 – ident: ref35 doi: 10.1016/j.automatica.2018.06.030 – ident: ref15 doi: 10.1016/j.neunet.2024.106266 – ident: ref14 doi: 10.1109/TNNLS.2022.3164909 – ident: ref26 doi: 10.1016/j.automatica.2017.01.032 – ident: ref33 doi: 10.1016/j.automatica.2017.07.013 – ident: ref44 doi: 10.1038/s41592-019-0690-6 – ident: ref7 doi: 10.1038/s41598-022-20979-x |
| SSID | ssj0000605649 |
| Score | 2.4798086 |
| Snippet | This article presents a novel dynamic programming approach to determine the robust controllability of Boolean control networks (BCNs) subject to stochastic... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 17448 |
| SubjectTerms | Boolean control networks (BCNs) Computational efficiency Computational modeling Controllability Dynamic programming Heuristic algorithms Perturbation methods robust controllability State feedback Sufficient conditions Time complexity Vectors |
| Title | Robust Controllability of Boolean Control Networks via Dynamic Programming |
| URI | https://ieeexplore.ieee.org/document/11006761 https://www.ncbi.nlm.nih.gov/pubmed/40388283 https://www.proquest.com/docview/3205817062 |
| Volume | 36 |
| WOSCitedRecordID | wos001494234100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEA66ePDi-7E-lgjepNombZMcfSIiRXSFvZU0mcLC2sp2V_Dfm0e74mEP3krbacN8085M5oXQeSRJFEegA8V0GMRMkUAUWgf2jyhDVtBE-2ETLMv4aCRe2mJ1VwsDAC75DC7toYvl61rN7VbZlW1vljLr7Kwyxnyx1mJDJTSGeerMXRKlJCCUjboimVBcDbPs-c24gyS5NBpWkNBO34ttKxTC6R-d5IasLLc3nd552PznirfQRmtg4msvEdtoBaodtNkNb8Dtt7yLnl7rYt7M8K3PVZ_4ft3fuC7xTV1PQFbdJZz5VPEGf40lvvMj7PGLT-z6MKpvD70_3A9vH4N2sEKgCGOzIJW8TJKUGSwA4pIBZdyYYppAERcFIwokJ0oWqTlTakFtkzMRUaUUaE5lSPdRr6orOES4pAJiiBWjUsWGQNrIpDCkwAvFCfTRRcfa_NP3z8id3xGK3GGSW0zyFpM-2rM8_L2zZV8fnXVw5Eb8bUxDVlDPm5ySMLE9BlPSRwcepwV1B-_Rkqceo3X7cp8xdoJ6s-kcTtGa-pqNm-nAyNiID5yM_QA4LMxO |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4hilQuLLQ8tqXgSr1VgcR24vgItIjHNkJ0kfYWOfZEWgkSxD4k_n39SEA9cOAWJZnIms_OjD2PD-BHomjCEzSRFiaOuNA0kpUxkfsjqlhULDWBbEIURT6ZyJuuWN3XwiCiTz7DI3fpY_mm1Qt3VHbs2ptlwm12PqSc0ySUa70cqcTWNc-8w0uTjEaUiUlfJhPL43FRjP7aDSFNj6yNlTR2_HvcNUOhOfvPKnmalbc9Tm95zgfvHPMmbHQuJjkJc2ILVrD5BIOevoF0q_kzXN221WI2J2chW_0-dOx-Jm1NTtv2HlXTPyJFSBafkeVUkV-BxJ7chNSuB2v8tuHu_Pf47CLqqBUiTYWYR5nK6zTNhEUDkdcCmcitM2YoVryqBNWocqpVldk7tZHMtTmTCdNao8mZitkOrDZtg3tAaiaRI9eCKc2tgHKxSWlFMa90TnEIP3vVlo-hg0bpdx6xLD0mpcOk7DAZwrbT4eubnfqG8L2Ho7QLwEU1VIPtYlYyGqeuy2BGh7AbcHqR7uH98sZXD-HjxfjPqBxdFtdfYd0NJOSP7cPq_GmB32BNL-fT2dOBn2n_ADFjzq0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Robust+Controllability+of+Boolean+Control+Networks+via+Dynamic+Programming&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Li%2C+Yakun&rft.au=Gao%2C+Shuhua&rft.au=Gao%2C+Yiming&rft.au=Wu%2C+Jianliang&rft.date=2025-09-01&rft.pub=IEEE&rft.issn=2162-237X&rft.volume=36&rft.issue=9&rft.spage=17448&rft.epage=17461&rft_id=info:doi/10.1109%2FTNNLS.2025.3559207&rft_id=info%3Apmid%2F40388283&rft.externalDocID=11006761 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon |