Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems
This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control prob...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2018-05, Vol.29 (5), p.2031-2036 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Wu, Yuhu Shen, Tielong |
| description | This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated. |
| doi_str_mv | 10.1109/TNNLS.2017.2661863 |
| format | Article |
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Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2017.2661863</identifier><identifier>PMID: 28287985</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Aerospace electronics ; Algebra ; Algorithms ; Boolean control networks ; Cost function ; Dynamical systems ; Heuristic algorithms ; infinite horizon optimal control ; Inventory management ; Iterative algorithms ; Learning systems ; Markov analysis ; Markov processes ; MDM2 protein ; Optimal control ; Optimization ; p53 Protein ; policy iteration ; semitensor product (STP) ; Stochasticity</subject><ispartof>IEEE transaction on neural networks and learning systems, 2018-05, Vol.29 (5), p.2031-2036</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-c9f348dab5f5aadeb15a1dbdb8d89a4a0b62fd19992c2b7742ef0900aa4f67213</citedby><cites>FETCH-LOGICAL-c351t-c9f348dab5f5aadeb15a1dbdb8d89a4a0b62fd19992c2b7742ef0900aa4f67213</cites><orcidid>0000-0001-9317-1404</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7872419$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7872419$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28287985$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Yuhu</creatorcontrib><creatorcontrib>Shen, Tielong</creatorcontrib><title>Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. 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To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.</description><subject>Aerospace electronics</subject><subject>Algebra</subject><subject>Algorithms</subject><subject>Boolean control networks</subject><subject>Cost function</subject><subject>Dynamical systems</subject><subject>Heuristic algorithms</subject><subject>infinite horizon optimal control</subject><subject>Inventory management</subject><subject>Iterative algorithms</subject><subject>Learning systems</subject><subject>Markov analysis</subject><subject>Markov processes</subject><subject>MDM2 protein</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>p53 Protein</subject><subject>policy iteration</subject><subject>semitensor product (STP)</subject><subject>Stochasticity</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEQhoMoKrV_QEEWvHhpTWZ383GU-gmlFVrFW8hmE13Z3dQkPey_d2trD85lBt5nhuFB6JzgMSFY3Cxns-liDJiwMVBKOE0P0CkQCiNIOT_cz-z9BA1D-MJ9UZzTTByjE-DAmeD5KXp7cXWlu-Q5Gq9i5drktv5wvoqfTWKdT-arWDWqTiaujd7VibPJIjr9qUKsdDJ1H5Xu07uuVc3vtOhCNE04Q0dW1cEMd32AXh_ul5On0XT--Dy5nY50mpM40sKmGS9VkdtcqdIUJFekLMqCl1yoTOGCgi2JEAI0FIxlYCwWGCuVWcqApAN0vb278u57bUKUTRW0qWvVGrcOknDGcqA45T169Q_9cmvf9t9JwCBIBhTSnoItpb0LwRsrV74X4DtJsNyIl7_i5Ua83Invly53p9dFY8r9yp_mHrjYApUxZh8zziAjIv0B_AOHiw</recordid><startdate>20180501</startdate><enddate>20180501</enddate><creator>Wu, Yuhu</creator><creator>Shen, Tielong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9317-1404</orcidid></search><sort><creationdate>20180501</creationdate><title>Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems</title><author>Wu, Yuhu ; Shen, Tielong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-c9f348dab5f5aadeb15a1dbdb8d89a4a0b62fd19992c2b7742ef0900aa4f67213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Aerospace electronics</topic><topic>Algebra</topic><topic>Algorithms</topic><topic>Boolean control networks</topic><topic>Cost function</topic><topic>Dynamical systems</topic><topic>Heuristic algorithms</topic><topic>infinite horizon optimal control</topic><topic>Inventory management</topic><topic>Iterative algorithms</topic><topic>Learning systems</topic><topic>Markov analysis</topic><topic>Markov processes</topic><topic>MDM2 protein</topic><topic>Optimal control</topic><topic>Optimization</topic><topic>p53 Protein</topic><topic>policy iteration</topic><topic>semitensor product (STP)</topic><topic>Stochasticity</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu, Yuhu</creatorcontrib><creatorcontrib>Shen, Tielong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu, Yuhu</au><au>Shen, Tielong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2018-05-01</date><risdate>2018</risdate><volume>29</volume><issue>5</issue><spage>2031</spage><epage>2036</epage><pages>2031-2036</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>28287985</pmid><doi>10.1109/TNNLS.2017.2661863</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0001-9317-1404</orcidid></addata></record> |
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| subjects | Aerospace electronics Algebra Algorithms Boolean control networks Cost function Dynamical systems Heuristic algorithms infinite horizon optimal control Inventory management Iterative algorithms Learning systems Markov analysis Markov processes MDM2 protein Optimal control Optimization p53 Protein policy iteration semitensor product (STP) Stochasticity |
| title | Policy Iteration Algorithm for Optimal Control of Stochastic Logical Dynamical Systems |
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