Finite-Time Synchronization of Clifford-Valued Neural Networks With Infinite Distributed Delays and Impulses
We study the issue of finite-time synchronization pertaining to a class of Clifford-valued neural networks with discrete and infinite distributed delays and impulse phenomena. Since multiplication of Clifford numbers is of non-commutativity, we decompose the original n -dimensional Clifford-valued...
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.111050-111061 |
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| creator | Boonsatit, N. Sriraman, R. Rojsiraphisal, T. Lim, C. P. Hammachukiattikul, P. Rajchakit, G. |
| description | We study the issue of finite-time synchronization pertaining to a class of Clifford-valued neural networks with discrete and infinite distributed delays and impulse phenomena. Since multiplication of Clifford numbers is of non-commutativity, we decompose the original n -dimensional Clifford-valued drive and response systems into the equivalent 2^{m} -dimensional real-valued counterparts. We then derive the finite-time synchronization criteria concerning the decomposed real-valued drive and response models through new Lyapunov-Krasovskii functional and suitable controller as well as new computational techniques. We also demonstrate the usefulness of the results through a simulation example. |
| doi_str_mv | 10.1109/ACCESS.2021.3102585 |
| format | Article |
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We also demonstrate the usefulness of the results through a simulation example.]]></description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3102585</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algebra ; Artificial neural networks ; Clifford-valued neural networks ; Commutativity ; Computational modeling ; Decomposition ; Delay effects ; Delays ; finite-time synchronization ; infinite distributed delay ; Lyapunov-Krasovskii fractional ; Multiplication ; Neural networks ; Synchronization ; Task analysis ; Time synchronization</subject><ispartof>IEEE access, 2021, Vol.9, p.111050-111061</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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P.</creatorcontrib><creatorcontrib>Hammachukiattikul, P.</creatorcontrib><creatorcontrib>Rajchakit, G.</creatorcontrib><title>Finite-Time Synchronization of Clifford-Valued Neural Networks With Infinite Distributed Delays and Impulses</title><title>IEEE access</title><addtitle>Access</addtitle><description><![CDATA[We study the issue of finite-time synchronization pertaining to a class of Clifford-valued neural networks with discrete and infinite distributed delays and impulse phenomena. Since multiplication of Clifford numbers is of non-commutativity, we decompose the original <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-dimensional Clifford-valued drive and response systems into the equivalent <inline-formula> <tex-math notation="LaTeX">2^{m} </tex-math></inline-formula>-dimensional real-valued counterparts. We then derive the finite-time synchronization criteria concerning the decomposed real-valued drive and response models through new Lyapunov-Krasovskii functional and suitable controller as well as new computational techniques. We also demonstrate the usefulness of the results through a simulation example.]]></description><subject>Algebra</subject><subject>Artificial neural networks</subject><subject>Clifford-valued neural networks</subject><subject>Commutativity</subject><subject>Computational modeling</subject><subject>Decomposition</subject><subject>Delay effects</subject><subject>Delays</subject><subject>finite-time synchronization</subject><subject>infinite distributed delay</subject><subject>Lyapunov-Krasovskii fractional</subject><subject>Multiplication</subject><subject>Neural networks</subject><subject>Synchronization</subject><subject>Task analysis</subject><subject>Time synchronization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUctu2zAQFIoWaJD4C3whkLNcPiTKPAZK0howmoPT9khQ5G5DRxZdkkLgfn3lKAi6l1kMZmYXmKJYMrpijKovN217t9utOOVsJRjl9br-UFxwJlUpaiE__rd_LhYp7ek064mqm4uiv_eDz1A--gOQ3WmwTzEM_q_JPgwkIGl7jxiiK3-afgRHvsMYTT9BfgnxOZFfPj-RzYCvKeTWpxx9N-ZJeQu9OSViBkc2h-PYJ0hXxSc007J4w8vix_3dY_ut3D583bQ329JWdJ1LxwRyg2AdIrpKCVDOooLGUWVRUgMdt84YiU4yxY1DUVfCCqZUp5hsxGWxmXNdMHt9jP5g4kkH4_UrEeJvbWL2tgcteacqKlhHkVZWys4ZWAuOTeeUbapqyrqes44x_BkhZb0PYxym9zWvJVWMNfKsErPKxpBSBHy_yqg-t6TnlvS5Jf3W0uRazi4PAO8OVdOmamrxD-KdkCE</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Boonsatit, N.</creator><creator>Sriraman, R.</creator><creator>Rojsiraphisal, T.</creator><creator>Lim, C. 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P. ; Hammachukiattikul, P. ; Rajchakit, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-d13f2afecdfffd493e9dcf9e7d09cf60aeb2cdaa6fd6192adf3543c3199b91673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algebra</topic><topic>Artificial neural networks</topic><topic>Clifford-valued neural networks</topic><topic>Commutativity</topic><topic>Computational modeling</topic><topic>Decomposition</topic><topic>Delay effects</topic><topic>Delays</topic><topic>finite-time synchronization</topic><topic>infinite distributed delay</topic><topic>Lyapunov-Krasovskii fractional</topic><topic>Multiplication</topic><topic>Neural networks</topic><topic>Synchronization</topic><topic>Task analysis</topic><topic>Time synchronization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boonsatit, N.</creatorcontrib><creatorcontrib>Sriraman, R.</creatorcontrib><creatorcontrib>Rojsiraphisal, T.</creatorcontrib><creatorcontrib>Lim, C. 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P.</au><au>Hammachukiattikul, P.</au><au>Rajchakit, G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Finite-Time Synchronization of Clifford-Valued Neural Networks With Infinite Distributed Delays and Impulses</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2021</date><risdate>2021</risdate><volume>9</volume><spage>111050</spage><epage>111061</epage><pages>111050-111061</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract><![CDATA[We study the issue of finite-time synchronization pertaining to a class of Clifford-valued neural networks with discrete and infinite distributed delays and impulse phenomena. Since multiplication of Clifford numbers is of non-commutativity, we decompose the original <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-dimensional Clifford-valued drive and response systems into the equivalent <inline-formula> <tex-math notation="LaTeX">2^{m} </tex-math></inline-formula>-dimensional real-valued counterparts. We then derive the finite-time synchronization criteria concerning the decomposed real-valued drive and response models through new Lyapunov-Krasovskii functional and suitable controller as well as new computational techniques. We also demonstrate the usefulness of the results through a simulation example.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3102585</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1384-4099</orcidid><orcidid>https://orcid.org/0000-0002-7062-0446</orcidid><orcidid>https://orcid.org/0000-0003-4191-9083</orcidid><orcidid>https://orcid.org/0000-0003-0267-110X</orcidid><orcidid>https://orcid.org/0000-0001-6053-6219</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algebra Artificial neural networks Clifford-valued neural networks Commutativity Computational modeling Decomposition Delay effects Delays finite-time synchronization infinite distributed delay Lyapunov-Krasovskii fractional Multiplication Neural networks Synchronization Task analysis Time synchronization |
| title | Finite-Time Synchronization of Clifford-Valued Neural Networks With Infinite Distributed Delays and Impulses |
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