Generating Grid Multi-Scroll Attractors in Memristive Neural Networks
Memristors are well suited as artificial nerve synapses owing to its unique memory function. This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed....
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| Veröffentlicht in: | IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2023-03, Vol.70 (3), p.1324-1336 |
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| container_title | IEEE transactions on circuits and systems. I, Regular papers |
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| creator | Lai, Qiang Wan, Zhiqiang Kuate, Paul Didier Kamdem |
| description | Memristors are well suited as artificial nerve synapses owing to its unique memory function. This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed. These memristive HNNs can generate multi-double-scroll chaotic attractors or grid multi-double-scroll chaotic attractors. The number of double scrolls in the attractors is controlled by the memristor. Equilibrium points analysis further reveals the generation mechanism of grid multi-double-scroll chaotic attractors. Moreover, numerical simulations indicate the existence of complex dynamics in the memristive HNNs, including extreme multistability and amplitude control. An approach to physically realize grid multi-double-scroll chaotic attractors is also given. Finally, an encryption scheme based on the proposed memristive HNN is designed to demonstrate application potential of the attractors. |
| doi_str_mv | 10.1109/TCSI.2022.3228566 |
| format | Article |
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This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed. These memristive HNNs can generate multi-double-scroll chaotic attractors or grid multi-double-scroll chaotic attractors. The number of double scrolls in the attractors is controlled by the memristor. Equilibrium points analysis further reveals the generation mechanism of grid multi-double-scroll chaotic attractors. Moreover, numerical simulations indicate the existence of complex dynamics in the memristive HNNs, including extreme multistability and amplitude control. An approach to physically realize grid multi-double-scroll chaotic attractors is also given. Finally, an encryption scheme based on the proposed memristive HNN is designed to demonstrate application potential of the attractors.</description><identifier>ISSN: 1549-8328</identifier><identifier>EISSN: 1558-0806</identifier><identifier>DOI: 10.1109/TCSI.2022.3228566</identifier><identifier>CODEN: ITCSCH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>amplitude control ; Biological neural networks ; extreme multistability ; grid multi-double-scroll attractor ; Hopfield neural network ; Hyperbolic functions ; Hysteresis ; Integrated circuit modeling ; Mathematical models ; Memristor ; Memristors ; Neural networks ; Neurons ; Synapses ; Voltage</subject><ispartof>IEEE transactions on circuits and systems. I, Regular papers, 2023-03, Vol.70 (3), p.1324-1336</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c271t-e8e3d8ae9d638c3abaeaafd3f7904c56d0c01126604ae7f925a31324df20635c3</citedby><cites>FETCH-LOGICAL-c271t-e8e3d8ae9d638c3abaeaafd3f7904c56d0c01126604ae7f925a31324df20635c3</cites><orcidid>0000-0002-7703-9793 ; 0000-0002-9485-4044 ; 0000-0002-7639-9930</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9991968$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9991968$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lai, Qiang</creatorcontrib><creatorcontrib>Wan, Zhiqiang</creatorcontrib><creatorcontrib>Kuate, Paul Didier Kamdem</creatorcontrib><title>Generating Grid Multi-Scroll Attractors in Memristive Neural Networks</title><title>IEEE transactions on circuits and systems. I, Regular papers</title><addtitle>TCSI</addtitle><description>Memristors are well suited as artificial nerve synapses owing to its unique memory function. This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed. These memristive HNNs can generate multi-double-scroll chaotic attractors or grid multi-double-scroll chaotic attractors. The number of double scrolls in the attractors is controlled by the memristor. Equilibrium points analysis further reveals the generation mechanism of grid multi-double-scroll chaotic attractors. Moreover, numerical simulations indicate the existence of complex dynamics in the memristive HNNs, including extreme multistability and amplitude control. An approach to physically realize grid multi-double-scroll chaotic attractors is also given. Finally, an encryption scheme based on the proposed memristive HNN is designed to demonstrate application potential of the attractors.</description><subject>amplitude control</subject><subject>Biological neural networks</subject><subject>extreme multistability</subject><subject>grid multi-double-scroll attractor</subject><subject>Hopfield neural network</subject><subject>Hyperbolic functions</subject><subject>Hysteresis</subject><subject>Integrated circuit modeling</subject><subject>Mathematical models</subject><subject>Memristor</subject><subject>Memristors</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Synapses</subject><subject>Voltage</subject><issn>1549-8328</issn><issn>1558-0806</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kLFOwzAQhi0EEqXwAIglEnPK2U4ce6yqUiq1MLTMlnEuyCVNiu2A-vYkasX03_D9d7qPkHsKE0pBPW1nm-WEAWMTzpjMhbggI5rnMgUJ4nKYM5VKzuQ1uQlhB8AUcDoi8wU26E10zWey8K5M1l0dXbqxvq3rZBqjNza2PiSuSda49y5E94PJK3be1H3E39Z_hVtyVZk64N05x-T9eb6dvaSrt8VyNl2llhU0piiRl9KgKgWXlpsPg8ZUJa8KBZnNRQkWKGVCQGawqBTLDaecZWXFQPDc8jF5PO09-Pa7wxD1ru1805_UrCiUKGhWqJ6iJ6p_IgSPlT54tzf-qCnowZYebOnBlj7b6jsPp45DxH9eKUWVkPwPTTNl9g</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Lai, Qiang</creator><creator>Wan, Zhiqiang</creator><creator>Kuate, Paul Didier Kamdem</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>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-7703-9793</orcidid><orcidid>https://orcid.org/0000-0002-9485-4044</orcidid><orcidid>https://orcid.org/0000-0002-7639-9930</orcidid></search><sort><creationdate>20230301</creationdate><title>Generating Grid Multi-Scroll Attractors in Memristive Neural Networks</title><author>Lai, Qiang ; Wan, Zhiqiang ; Kuate, Paul Didier Kamdem</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c271t-e8e3d8ae9d638c3abaeaafd3f7904c56d0c01126604ae7f925a31324df20635c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>amplitude control</topic><topic>Biological neural networks</topic><topic>extreme multistability</topic><topic>grid multi-double-scroll attractor</topic><topic>Hopfield neural network</topic><topic>Hyperbolic functions</topic><topic>Hysteresis</topic><topic>Integrated circuit modeling</topic><topic>Mathematical models</topic><topic>Memristor</topic><topic>Memristors</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Synapses</topic><topic>Voltage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lai, Qiang</creatorcontrib><creatorcontrib>Wan, Zhiqiang</creatorcontrib><creatorcontrib>Kuate, Paul Didier Kamdem</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>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on circuits and systems. I, Regular papers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lai, Qiang</au><au>Wan, Zhiqiang</au><au>Kuate, Paul Didier Kamdem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generating Grid Multi-Scroll Attractors in Memristive Neural Networks</atitle><jtitle>IEEE transactions on circuits and systems. I, Regular papers</jtitle><stitle>TCSI</stitle><date>2023-03-01</date><risdate>2023</risdate><volume>70</volume><issue>3</issue><spage>1324</spage><epage>1336</epage><pages>1324-1336</pages><issn>1549-8328</issn><eissn>1558-0806</eissn><coden>ITCSCH</coden><abstract>Memristors are well suited as artificial nerve synapses owing to its unique memory function. This paper establishes a novel flux-controlled memristor model using hyperbolic function series. By taking the memristor as synapses in a Hopfield neural network (HNN), three memristive HNNs are constructed. These memristive HNNs can generate multi-double-scroll chaotic attractors or grid multi-double-scroll chaotic attractors. The number of double scrolls in the attractors is controlled by the memristor. Equilibrium points analysis further reveals the generation mechanism of grid multi-double-scroll chaotic attractors. Moreover, numerical simulations indicate the existence of complex dynamics in the memristive HNNs, including extreme multistability and amplitude control. An approach to physically realize grid multi-double-scroll chaotic attractors is also given. Finally, an encryption scheme based on the proposed memristive HNN is designed to demonstrate application potential of the attractors.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSI.2022.3228566</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-7703-9793</orcidid><orcidid>https://orcid.org/0000-0002-9485-4044</orcidid><orcidid>https://orcid.org/0000-0002-7639-9930</orcidid></addata></record> |
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| ispartof | IEEE transactions on circuits and systems. I, Regular papers, 2023-03, Vol.70 (3), p.1324-1336 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | amplitude control Biological neural networks extreme multistability grid multi-double-scroll attractor Hopfield neural network Hyperbolic functions Hysteresis Integrated circuit modeling Mathematical models Memristor Memristors Neural networks Neurons Synapses Voltage |
| title | Generating Grid Multi-Scroll Attractors in Memristive Neural Networks |
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