A Disturbance Rejection Framework for Finite-Time and Fixed-Time Stabilization of Delayed Memristive Neural Networks
This paper proposes a unified framework to design sliding-mode control for stabilization of delayed memristive neural networks (DMNNs) with external disturbances. Under the presented framework, finite-time stabilization, and fixed-time stabilization of the controlled DMNNs can be, respectively, obta...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2021-02, Vol.51 (2), p.905-915 |
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| container_title | IEEE transactions on systems, man, and cybernetics. Systems |
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| creator | Wang, Leimin Zeng, Zhigang Ge, Ming-Feng |
| description | This paper proposes a unified framework to design sliding-mode control for stabilization of delayed memristive neural networks (DMNNs) with external disturbances. Under the presented framework, finite-time stabilization, and fixed-time stabilization of the controlled DMNNs can be, respectively, obtained by choosing different values for a specific control parameter. It is proved that the system responses can be made reaching the designed sliding-mode surface in finite and fixed time, and then stay on it. Moreover, it also illustrates that the inevitable external disturbances can be rejected by the designed sliding-mode control. Finally, the efficiency and superiority of the obtained main results are verified by comparisons with related works and numerical simulations. |
| doi_str_mv | 10.1109/TSMC.2018.2888867 |
| format | Article |
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Finally, the efficiency and superiority of the obtained main results are verified by comparisons with related works and numerical simulations.</description><subject>Convergence</subject><subject>Delayed memristive neural networks (DMNNs)</subject><subject>Delays</subject><subject>Disturbances</subject><subject>finite-time stabilization (FTS)</subject><subject>fixed-time stabilization (FxTS)</subject><subject>Geology</subject><subject>Memristors</subject><subject>Neural networks</subject><subject>Sliding mode control</subject><subject>Stabilization</subject><subject>Switches</subject><subject>Synchronization</subject><subject>unified framework</subject><issn>2168-2216</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UMtOwzAQjBBIVNAPQFwscU7xo3HsY9VSQGpBouUc2fFGcsmjOA5Qvh6HVN3L7kgzs5qJohuCJ4Rgeb_drOcTiomYUBGGp2fRiBIuYkoZPT_dhF9G47bdYYwJFZxhPor8DC1s6zunVZ0DeoMd5N42NVo6VcF34z5Q0Ti0tLX1EG9tBUjVJuAfMAPceKVtaX_Vv6wp0AJKdQCD1lC5YG2_AL1A51QZlu8d2-voolBlC-Pjvorelw_b-VO8en18ns9WcU4l8zFLDBGUcTM1mCSJxrkUGmQiseY4p0UfQzIi88Lk2MikYESbRIEujE51CHgV3Q2-e9d8dtD6bNd0rg4vMzpNhWAsETiwyMDKXdO2Dops72yl3CEjOOv7zfp-s77f7Nhv0NwOGgsAJ77gGKecsz9HwHde</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Wang, Leimin</creator><creator>Zeng, Zhigang</creator><creator>Ge, Ming-Feng</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>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-0663-3365</orcidid><orcidid>https://orcid.org/0000-0002-6828-0147</orcidid><orcidid>https://orcid.org/0000-0003-4587-3588</orcidid></search><sort><creationdate>20210201</creationdate><title>A Disturbance Rejection Framework for Finite-Time and Fixed-Time Stabilization of Delayed Memristive Neural Networks</title><author>Wang, Leimin ; Zeng, Zhigang ; Ge, Ming-Feng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-35d18236d4d0155b0c98be9590b60c2f00129319cfdc0d95f31bd5aebfdb7b863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Convergence</topic><topic>Delayed memristive neural networks (DMNNs)</topic><topic>Delays</topic><topic>Disturbances</topic><topic>finite-time stabilization (FTS)</topic><topic>fixed-time stabilization (FxTS)</topic><topic>Geology</topic><topic>Memristors</topic><topic>Neural networks</topic><topic>Sliding mode control</topic><topic>Stabilization</topic><topic>Switches</topic><topic>Synchronization</topic><topic>unified framework</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Leimin</creatorcontrib><creatorcontrib>Zeng, Zhigang</creatorcontrib><creatorcontrib>Ge, Ming-Feng</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>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace 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 systems, man, and cybernetics. Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Leimin</au><au>Zeng, Zhigang</au><au>Ge, Ming-Feng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Disturbance Rejection Framework for Finite-Time and Fixed-Time Stabilization of Delayed Memristive Neural Networks</atitle><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle><stitle>TSMC</stitle><date>2021-02-01</date><risdate>2021</risdate><volume>51</volume><issue>2</issue><spage>905</spage><epage>915</epage><pages>905-915</pages><issn>2168-2216</issn><eissn>2168-2232</eissn><coden>ITSMFE</coden><abstract>This paper proposes a unified framework to design sliding-mode control for stabilization of delayed memristive neural networks (DMNNs) with external disturbances. Under the presented framework, finite-time stabilization, and fixed-time stabilization of the controlled DMNNs can be, respectively, obtained by choosing different values for a specific control parameter. It is proved that the system responses can be made reaching the designed sliding-mode surface in finite and fixed time, and then stay on it. Moreover, it also illustrates that the inevitable external disturbances can be rejected by the designed sliding-mode control. Finally, the efficiency and superiority of the obtained main results are verified by comparisons with related works and numerical simulations.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSMC.2018.2888867</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0663-3365</orcidid><orcidid>https://orcid.org/0000-0002-6828-0147</orcidid><orcidid>https://orcid.org/0000-0003-4587-3588</orcidid></addata></record> |
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| language | eng |
| recordid | cdi_crossref_primary_10_1109_TSMC_2018_2888867 |
| source | IEEE Electronic Library (IEL) |
| subjects | Convergence Delayed memristive neural networks (DMNNs) Delays Disturbances finite-time stabilization (FTS) fixed-time stabilization (FxTS) Geology Memristors Neural networks Sliding mode control Stabilization Switches Synchronization unified framework |
| title | A Disturbance Rejection Framework for Finite-Time and Fixed-Time Stabilization of Delayed Memristive Neural Networks |
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