Parameter Control Methods for Discrete Memristive Maps with Network Structure
Discrete memristors (DMs) have attracted increasing interest in recent years and can be applied in chaotic circuits and neuromorphic systems. To better enrich and enhance the kinetics and performance of DM maps, this article introduces two improved control methods to construct a series of DM maps wi...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2024-05, Vol.20 (5), p.7194-7204 |
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| creator | Yuan, Fang Zhang, Shuting Xing, Guibin Deng, Yue |
| description | Discrete memristors (DMs) have attracted increasing interest in recent years and can be applied in chaotic circuits and neuromorphic systems. To better enrich and enhance the kinetics and performance of DM maps, this article introduces two improved control methods to construct a series of DM maps with different network typologies by coupling DM seed maps. Some specific examples are demonstrated to back up these methods. Due to the effect and specific constructs of memristors, the new coupling DM maps will have infinite invariant points with higher dimensions, whose advantages of larger parameter space and expended chaotic ranges are evaluated by dynamic simulations. Besides, to investigate the application of the proposed methods, these new DM maps are implemented on the DSP platform, and the pseudorandom number generator is designed, as well as their high randomness is indicated by the strict test results. |
| doi_str_mv | 10.1109/TII.2024.3353798 |
| format | Article |
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To better enrich and enhance the kinetics and performance of DM maps, this article introduces two improved control methods to construct a series of DM maps with different network typologies by coupling DM seed maps. Some specific examples are demonstrated to back up these methods. Due to the effect and specific constructs of memristors, the new coupling DM maps will have infinite invariant points with higher dimensions, whose advantages of larger parameter space and expended chaotic ranges are evaluated by dynamic simulations. Besides, to investigate the application of the proposed methods, these new DM maps are implemented on the DSP platform, and the pseudorandom number generator is designed, as well as their high randomness is indicated by the strict test results.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2024.3353798</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Behavioral sciences ; Bidirectional parameter control ; chaotic behavior ; Chaotic communication ; Control methods ; Coupling ; Couplings ; discrete memristor (DM) ; Informatics ; Integrated circuit modeling ; Memristors ; Modulation ; Parameter estimation ; Parameters ; Pseudorandom ; unilateral parameter control</subject><ispartof>IEEE transactions on industrial informatics, 2024-05, Vol.20 (5), p.7194-7204</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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To better enrich and enhance the kinetics and performance of DM maps, this article introduces two improved control methods to construct a series of DM maps with different network typologies by coupling DM seed maps. Some specific examples are demonstrated to back up these methods. Due to the effect and specific constructs of memristors, the new coupling DM maps will have infinite invariant points with higher dimensions, whose advantages of larger parameter space and expended chaotic ranges are evaluated by dynamic simulations. Besides, to investigate the application of the proposed methods, these new DM maps are implemented on the DSP platform, and the pseudorandom number generator is designed, as well as their high randomness is indicated by the strict test results.</description><subject>Behavioral sciences</subject><subject>Bidirectional parameter control</subject><subject>chaotic behavior</subject><subject>Chaotic communication</subject><subject>Control methods</subject><subject>Coupling</subject><subject>Couplings</subject><subject>discrete memristor (DM)</subject><subject>Informatics</subject><subject>Integrated circuit modeling</subject><subject>Memristors</subject><subject>Modulation</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>Pseudorandom</subject><subject>unilateral parameter control</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Lw0AQxRdRsFbvHjwEPKfOfiXZo9SvQquC9bwk6YSmNt06u7H437ulPXiax5v3ZuDH2DWHEedg7uaTyUiAUCMptcxNccIG3CieAmg4jVprnkoB8pxdeL8CkDlIM2Cz95LKDgNSMnabQG6dzDAs3cInjaPkofU1xW00O2p9aH-iLLc-2bVhmbxi2Dn6Sj4C9XXoCS_ZWVOuPV4d55B9Pj3Oxy_p9O15Mr6fprVQOqRKN0qgwTJruI7CLBosikLnpopWLkWJecW1KQw0XOnMcCgqKTGDnKtFBXLIbg93t-S-e_TBrlxPm_jSStBCAGRGxRQcUjU57wkbu6W2K-nXcrB7aDZCs3to9ggtVm4OlRYR_8UVL0AZ-QcvkmeO</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Yuan, Fang</creator><creator>Zhang, Shuting</creator><creator>Xing, Guibin</creator><creator>Deng, Yue</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>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-9963-5783</orcidid><orcidid>https://orcid.org/0000-0002-5483-1948</orcidid><orcidid>https://orcid.org/0000-0003-2017-6030</orcidid><orcidid>https://orcid.org/0000-0002-3330-070X</orcidid></search><sort><creationdate>20240501</creationdate><title>Parameter Control Methods for Discrete Memristive Maps with Network Structure</title><author>Yuan, Fang ; Zhang, Shuting ; Xing, Guibin ; Deng, Yue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c245t-45f42e9ea6f152e99dfe888579ba6f732ae7b159890f14569108b33e60714db03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Behavioral sciences</topic><topic>Bidirectional parameter control</topic><topic>chaotic behavior</topic><topic>Chaotic communication</topic><topic>Control methods</topic><topic>Coupling</topic><topic>Couplings</topic><topic>discrete memristor (DM)</topic><topic>Informatics</topic><topic>Integrated circuit modeling</topic><topic>Memristors</topic><topic>Modulation</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>Pseudorandom</topic><topic>unilateral parameter control</topic><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Fang</creatorcontrib><creatorcontrib>Zhang, Shuting</creatorcontrib><creatorcontrib>Xing, Guibin</creatorcontrib><creatorcontrib>Deng, Yue</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>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on industrial informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yuan, Fang</au><au>Zhang, Shuting</au><au>Xing, Guibin</au><au>Deng, Yue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parameter Control Methods for Discrete Memristive Maps with Network Structure</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><date>2024-05-01</date><risdate>2024</risdate><volume>20</volume><issue>5</issue><spage>7194</spage><epage>7204</epage><pages>7194-7204</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract>Discrete memristors (DMs) have attracted increasing interest in recent years and can be applied in chaotic circuits and neuromorphic systems. To better enrich and enhance the kinetics and performance of DM maps, this article introduces two improved control methods to construct a series of DM maps with different network typologies by coupling DM seed maps. Some specific examples are demonstrated to back up these methods. Due to the effect and specific constructs of memristors, the new coupling DM maps will have infinite invariant points with higher dimensions, whose advantages of larger parameter space and expended chaotic ranges are evaluated by dynamic simulations. 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| subjects | Behavioral sciences Bidirectional parameter control chaotic behavior Chaotic communication Control methods Coupling Couplings discrete memristor (DM) Informatics Integrated circuit modeling Memristors Modulation Parameter estimation Parameters Pseudorandom unilateral parameter control |
| title | Parameter Control Methods for Discrete Memristive Maps with Network Structure |
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