Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks

In this paper, a fractional model of a special structure of bidirectional associative memory (BAM) neural networks called tri-diagonal BAM neural networks (TdBAMNNs) is considered. The Hopf bifurcation analysis is made for the proposed fractional system in the presence of leakage and communication d...

Full description

Saved in:
Bibliographic Details
Published in:Neural processing letters 2023-12, Vol.55 (6), p.8095-8113
Main Authors: Rakshana, M., Balasubramaniam, P.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Associative memory
Communication
Complex Systems
Computational Intelligence
Computer Science
Eigenvalues
Equilibrium
Hopf bifurcation
Investigations
Mathematical models
Neural networks
Recurrent neural networks
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-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3
cites cdi_FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3
container_end_page 8113
container_issue 6
container_start_page 8095
container_title Neural processing letters
container_volume 55
creator Rakshana, M.
Balasubramaniam, P.
description In this paper, a fractional model of a special structure of bidirectional associative memory (BAM) neural networks called tri-diagonal BAM neural networks (TdBAMNNs) is considered. The Hopf bifurcation analysis is made for the proposed fractional system in the presence of leakage and communication delays. The feasibility of the obtained theoretical results is verified by numerical simulations.
doi_str_mv 10.1007/s11063-023-11302-4
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2918354265</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918354265</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3</originalsourceid><addsrcrecordid>eNp9kEtPwzAQhC0EEqXwBzhF4mzwK3Fy7IO2SKVcisTNWuwNail1sROh_nscgsSN0452Z1ajj5Brzm45Y_oucs4KSZmQlHPJBFUnZMBzLanW8uU0aakZVYXg5-Qixi1jKSbYgEwX_lBn403dBgvNxu8zX2dz3GOAXTYLYLtdklPcwRFdtnbj0WO2wra7r7D58uE9XpKzGnYRr37nkDzP7teTBV0-zR8moyW1klcNdfBaFKCl1hY0F5DnziLywgqmoawsSIUSU9PSVrZkqLlyDAoJttTKaSeH5Kb_ewj-s8XYmK1vQ6oXjah4KXMlijy5RO-ywccYsDaHsPmAcDScmY6W6WmZRMv80DIqhWQfism8f8Pw9_qf1DcJiGu8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918354265</pqid></control><display><type>article</type><title>Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks</title><source>Springer Nature</source><creator>Rakshana, M. ; Balasubramaniam, P.</creator><creatorcontrib>Rakshana, M. ; Balasubramaniam, P.</creatorcontrib><description>In this paper, a fractional model of a special structure of bidirectional associative memory (BAM) neural networks called tri-diagonal BAM neural networks (TdBAMNNs) is considered. The Hopf bifurcation analysis is made for the proposed fractional system in the presence of leakage and communication delays. The feasibility of the obtained theoretical results is verified by numerical simulations.</description><identifier>ISSN: 1370-4621</identifier><identifier>EISSN: 1573-773X</identifier><identifier>DOI: 10.1007/s11063-023-11302-4</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial Intelligence ; Associative memory ; Communication ; Complex Systems ; Computational Intelligence ; Computer Science ; Eigenvalues ; Equilibrium ; Hopf bifurcation ; Investigations ; Mathematical models ; Neural networks ; Recurrent neural networks</subject><ispartof>Neural processing letters, 2023-12, Vol.55 (6), p.8095-8113</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3</citedby><cites>FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3</cites></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></links><search><creatorcontrib>Rakshana, M.</creatorcontrib><creatorcontrib>Balasubramaniam, P.</creatorcontrib><title>Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks</title><title>Neural processing letters</title><addtitle>Neural Process Lett</addtitle><description>In this paper, a fractional model of a special structure of bidirectional associative memory (BAM) neural networks called tri-diagonal BAM neural networks (TdBAMNNs) is considered. The Hopf bifurcation analysis is made for the proposed fractional system in the presence of leakage and communication delays. The feasibility of the obtained theoretical results is verified by numerical simulations.</description><subject>Artificial Intelligence</subject><subject>Associative memory</subject><subject>Communication</subject><subject>Complex Systems</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Eigenvalues</subject><subject>Equilibrium</subject><subject>Hopf bifurcation</subject><subject>Investigations</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Recurrent neural networks</subject><issn>1370-4621</issn><issn>1573-773X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEqXwBzhF4mzwK3Fy7IO2SKVcisTNWuwNail1sROh_nscgsSN0452Z1ajj5Brzm45Y_oucs4KSZmQlHPJBFUnZMBzLanW8uU0aakZVYXg5-Qixi1jKSbYgEwX_lBn403dBgvNxu8zX2dz3GOAXTYLYLtdklPcwRFdtnbj0WO2wra7r7D58uE9XpKzGnYRr37nkDzP7teTBV0-zR8moyW1klcNdfBaFKCl1hY0F5DnziLywgqmoawsSIUSU9PSVrZkqLlyDAoJttTKaSeH5Kb_ewj-s8XYmK1vQ6oXjah4KXMlijy5RO-ywccYsDaHsPmAcDScmY6W6WmZRMv80DIqhWQfism8f8Pw9_qf1DcJiGu8</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Rakshana, M.</creator><creator>Balasubramaniam, P.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope></search><sort><creationdate>20231201</creationdate><title>Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks</title><author>Rakshana, M. ; Balasubramaniam, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>Associative memory</topic><topic>Communication</topic><topic>Complex Systems</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Eigenvalues</topic><topic>Equilibrium</topic><topic>Hopf bifurcation</topic><topic>Investigations</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Recurrent neural networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rakshana, M.</creatorcontrib><creatorcontrib>Balasubramaniam, P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><jtitle>Neural processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rakshana, M.</au><au>Balasubramaniam, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks</atitle><jtitle>Neural processing letters</jtitle><stitle>Neural Process Lett</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>55</volume><issue>6</issue><spage>8095</spage><epage>8113</epage><pages>8095-8113</pages><issn>1370-4621</issn><eissn>1573-773X</eissn><abstract>In this paper, a fractional model of a special structure of bidirectional associative memory (BAM) neural networks called tri-diagonal BAM neural networks (TdBAMNNs) is considered. The Hopf bifurcation analysis is made for the proposed fractional system in the presence of leakage and communication delays. The feasibility of the obtained theoretical results is verified by numerical simulations.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11063-023-11302-4</doi><tpages>19</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1370-4621
ispartof Neural processing letters, 2023-12, Vol.55 (6), p.8095-8113
issn 1370-4621
1573-773X
language eng
recordid cdi_proquest_journals_2918354265
source Springer Nature
subjects Artificial Intelligence
Associative memory
Communication
Complex Systems
Computational Intelligence
Computer Science
Eigenvalues
Equilibrium
Hopf bifurcation
Investigations
Mathematical models
Neural networks
Recurrent neural networks
title Hopf Bifurcation of General Fractional Delayed TdBAM Neural Networks
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T03%3A25%3A32IST&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=Hopf%20Bifurcation%20of%20General%20Fractional%20Delayed%20TdBAM%20Neural%20Networks&rft.jtitle=Neural%20processing%20letters&rft.au=Rakshana,%20M.&rft.date=2023-12-01&rft.volume=55&rft.issue=6&rft.spage=8095&rft.epage=8113&rft.pages=8095-8113&rft.issn=1370-4621&rft.eissn=1573-773X&rft_id=info:doi/10.1007/s11063-023-11302-4&rft_dat=%3Cproquest_cross%3E2918354265%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c319t-dab66a7377ca712a55dcee16c207a89ca34e3e1378c9c80e714d0a63ac874d7d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2918354265&rft_id=info:pmid/&rfr_iscdi=true