Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel an...

Full description

Saved in:
Bibliographic Details
Published in:Neural networks 2016-03, Vol.75, p.162-172
Main Authors: Yang, Shiju, Li, Chuandong, Huang, Tingwen
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Exponential stabilization
Fuzzy
Fuzzy Logic
Fuzzy model of memristive neural networks (MNNs)
Intermittent control
Knowledge
Mathematical models
Models, Theoretical
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Resistors
Stabilization
Synchronism
Synchronization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3
cites cdi_FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3
container_end_page 172
container_issue
container_start_page 162
container_title Neural networks
container_volume 75
creator Yang, Shiju
Li, Chuandong
Huang, Tingwen
description The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.
doi_str_mv 10.1016/j.neunet.2015.12.003
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1808091625</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0893608015002701</els_id><sourcerecordid>1767913689</sourcerecordid><originalsourceid>FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3</originalsourceid><addsrcrecordid>eNqNkU-PFCEQxYnRuOPoNzCGo5duge6m4WJiNuufZBMveiY0VEdGGkag1-05-dFlM7se1bqQFK_qVd4PoZeUtJRQ_ubQBlgDlJYROrSUtYR0j9COilE2bBTsMdoRIbuGE0Eu0LOcD4QQLvruKbpgfJRjP9Id-nV1e4wBQnHa41z05Lw76eJiwDpYnLdgvqUYHnpzTHheT6cNL9GCx3HGCyzJ5eJuANeDUl1Tj_oZ0_eMpw0fIblondHeb9iFAmlxpVQ_bGIoKfrn6MmsfYYX9-8efX1_9eXyY3P9-cOny3fXjemZKA1j1BLLJztBN3ScWin0bIdBQy0jhnkwfKw_ujeWDN04iW6mAPMkoQdpoNuj1-e9xxR_rJCLWlw24L0OENesqKg5ScrZ8G_pKInsqeT8P6Q1adrxCmKP-rPUpJhzglkdk1t02hQl6o6oOqgzUXVHVFGmKtE69ureYZ0WsH-GHhBWwduzAGp6Nw6SysZBMGBdAlOUje7vDr8BNO-4wg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1767913689</pqid></control><display><type>article</type><title>Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control</title><source>Elsevier:Jisc Collections:Elsevier Read and Publish Agreement 2022-2024:Freedom Collection (Reading list)</source><creator>Yang, Shiju ; Li, Chuandong ; Huang, Tingwen</creator><creatorcontrib>Yang, Shiju ; Li, Chuandong ; Huang, Tingwen</creatorcontrib><description>The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.</description><identifier>ISSN: 0893-6080</identifier><identifier>EISSN: 1879-2782</identifier><identifier>DOI: 10.1016/j.neunet.2015.12.003</identifier><identifier>PMID: 26797471</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Computer simulation ; Exponential stabilization ; Fuzzy ; Fuzzy Logic ; Fuzzy model of memristive neural networks (MNNs) ; Intermittent control ; Knowledge ; Mathematical models ; Models, Theoretical ; Neural networks ; Neural Networks (Computer) ; Nonlinear Dynamics ; Resistors ; Stabilization ; Synchronism ; Synchronization</subject><ispartof>Neural networks, 2016-03, Vol.75, p.162-172</ispartof><rights>2015 Elsevier Ltd</rights><rights>Copyright © 2015 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3</citedby><cites>FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3</cites><orcidid>0000-0001-6155-4849</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26797471$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Shiju</creatorcontrib><creatorcontrib>Li, Chuandong</creatorcontrib><creatorcontrib>Huang, Tingwen</creatorcontrib><title>Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control</title><title>Neural networks</title><addtitle>Neural Netw</addtitle><description>The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.</description><subject>Algorithms</subject><subject>Computer simulation</subject><subject>Exponential stabilization</subject><subject>Fuzzy</subject><subject>Fuzzy Logic</subject><subject>Fuzzy model of memristive neural networks (MNNs)</subject><subject>Intermittent control</subject><subject>Knowledge</subject><subject>Mathematical models</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Nonlinear Dynamics</subject><subject>Resistors</subject><subject>Stabilization</subject><subject>Synchronism</subject><subject>Synchronization</subject><issn>0893-6080</issn><issn>1879-2782</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqNkU-PFCEQxYnRuOPoNzCGo5duge6m4WJiNuufZBMveiY0VEdGGkag1-05-dFlM7se1bqQFK_qVd4PoZeUtJRQ_ubQBlgDlJYROrSUtYR0j9COilE2bBTsMdoRIbuGE0Eu0LOcD4QQLvruKbpgfJRjP9Id-nV1e4wBQnHa41z05Lw76eJiwDpYnLdgvqUYHnpzTHheT6cNL9GCx3HGCyzJ5eJuANeDUl1Tj_oZ0_eMpw0fIblondHeb9iFAmlxpVQ_bGIoKfrn6MmsfYYX9-8efX1_9eXyY3P9-cOny3fXjemZKA1j1BLLJztBN3ScWin0bIdBQy0jhnkwfKw_ujeWDN04iW6mAPMkoQdpoNuj1-e9xxR_rJCLWlw24L0OENesqKg5ScrZ8G_pKInsqeT8P6Q1adrxCmKP-rPUpJhzglkdk1t02hQl6o6oOqgzUXVHVFGmKtE69ureYZ0WsH-GHhBWwduzAGp6Nw6SysZBMGBdAlOUje7vDr8BNO-4wg</recordid><startdate>201603</startdate><enddate>201603</enddate><creator>Yang, Shiju</creator><creator>Li, Chuandong</creator><creator>Huang, Tingwen</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-6155-4849</orcidid></search><sort><creationdate>201603</creationdate><title>Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control</title><author>Yang, Shiju ; Li, Chuandong ; Huang, Tingwen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Computer simulation</topic><topic>Exponential stabilization</topic><topic>Fuzzy</topic><topic>Fuzzy Logic</topic><topic>Fuzzy model of memristive neural networks (MNNs)</topic><topic>Intermittent control</topic><topic>Knowledge</topic><topic>Mathematical models</topic><topic>Models, Theoretical</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Nonlinear Dynamics</topic><topic>Resistors</topic><topic>Stabilization</topic><topic>Synchronism</topic><topic>Synchronization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Shiju</creatorcontrib><creatorcontrib>Li, Chuandong</creatorcontrib><creatorcontrib>Huang, Tingwen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</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>Neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Shiju</au><au>Li, Chuandong</au><au>Huang, Tingwen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control</atitle><jtitle>Neural networks</jtitle><addtitle>Neural Netw</addtitle><date>2016-03</date><risdate>2016</risdate><volume>75</volume><spage>162</spage><epage>172</epage><pages>162-172</pages><issn>0893-6080</issn><eissn>1879-2782</eissn><abstract>The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>26797471</pmid><doi>10.1016/j.neunet.2015.12.003</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6155-4849</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0893-6080
ispartof Neural networks, 2016-03, Vol.75, p.162-172
issn 0893-6080
1879-2782
language eng
recordid cdi_proquest_miscellaneous_1808091625
source Elsevier:Jisc Collections:Elsevier Read and Publish Agreement 2022-2024:Freedom Collection (Reading list)
subjects Algorithms
Computer simulation
Exponential stabilization
Fuzzy
Fuzzy Logic
Fuzzy model of memristive neural networks (MNNs)
Intermittent control
Knowledge
Mathematical models
Models, Theoretical
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Resistors
Stabilization
Synchronism
Synchronization
title Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T09%3A18%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exponential%20stabilization%20and%20synchronization%20for%20fuzzy%20model%20of%20memristive%20neural%20networks%20by%20periodically%20intermittent%20control&rft.jtitle=Neural%20networks&rft.au=Yang,%20Shiju&rft.date=2016-03&rft.volume=75&rft.spage=162&rft.epage=172&rft.pages=162-172&rft.issn=0893-6080&rft.eissn=1879-2782&rft_id=info:doi/10.1016/j.neunet.2015.12.003&rft_dat=%3Cproquest_cross%3E1767913689%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c428t-221d0d6bdbe35361d98afd55aeeeec85f5c67353a4cd0537b83f1eefb9e4e9ce3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1767913689&rft_id=info:pmid/26797471&rfr_iscdi=true