Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons

Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons

In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of dynamic systems, measurement, and control measurement, and control, 2017-08, Vol.139 (8)
1. Verfasser: Javidmanesh, Elham
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 8
container_start_page
container_title Journal of dynamic systems, measurement, and control
container_volume 139
creator Javidmanesh, Elham
description In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.
doi_str_mv 10.1115/1.4036229
format Article
fullrecord <record><control><sourceid>asme_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1115_1_4036229</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>384816</sourcerecordid><originalsourceid>FETCH-LOGICAL-a249t-2a34196fd00559502bf89c8aac6e1f316f353315bb16a05ec66a7f7199c95fb83</originalsourceid><addsrcrecordid>eNotkDFPwzAQhS0EEqUwMLN4ZUjx2bEbj6WUglTKAIgxsl1buCQxshNQN346Ce100rvvnu49hC6BTACA38AkJ0xQKo_QCDgtMklocYxGhFCakZzlp-gspS0hwBgXI_S7rIJWFX5plfaVb3dYNRt8610XjWp9aLBv8J2t1M4O8sZHawa5P5mlFIzvoW-Ln2wd4g6vbRf7zdq2PyF-Jvzu24_eEM-i9m1UA9HV2kYc3D8bmnSOTpyqkr04zDF6u1-8zh-y1fPycT5bZYrmss2oYjlI4TaEcC45odoV0hRKGWHBMRCOccaAaw1CEW6NEGrqpiClkdzpgo3R9d7XxJBStK78ir7uXyqBlEN1JZSH6nr2as-qVNtyG7rY500lK_ICBPsDCJZrIw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons</title><source>Alma/SFX Local Collection</source><source>ASME Transactions Journals (Current)</source><creator>Javidmanesh, Elham</creator><creatorcontrib>Javidmanesh, Elham</creatorcontrib><description>In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.</description><identifier>ISSN: 0022-0434</identifier><identifier>EISSN: 1528-9028</identifier><identifier>DOI: 10.1115/1.4036229</identifier><language>eng</language><publisher>ASME</publisher><ispartof>Journal of dynamic systems, measurement, and control, 2017-08, Vol.139 (8)</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a249t-2a34196fd00559502bf89c8aac6e1f316f353315bb16a05ec66a7f7199c95fb83</citedby><cites>FETCH-LOGICAL-a249t-2a34196fd00559502bf89c8aac6e1f316f353315bb16a05ec66a7f7199c95fb83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902,38497</link.rule.ids></links><search><creatorcontrib>Javidmanesh, Elham</creatorcontrib><title>Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons</title><title>Journal of dynamic systems, measurement, and control</title><addtitle>J. Dyn. Sys., Meas., Control</addtitle><description>In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.</description><issn>0022-0434</issn><issn>1528-9028</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNotkDFPwzAQhS0EEqUwMLN4ZUjx2bEbj6WUglTKAIgxsl1buCQxshNQN346Ce100rvvnu49hC6BTACA38AkJ0xQKo_QCDgtMklocYxGhFCakZzlp-gspS0hwBgXI_S7rIJWFX5plfaVb3dYNRt8610XjWp9aLBv8J2t1M4O8sZHawa5P5mlFIzvoW-Ln2wd4g6vbRf7zdq2PyF-Jvzu24_eEM-i9m1UA9HV2kYc3D8bmnSOTpyqkr04zDF6u1-8zh-y1fPycT5bZYrmss2oYjlI4TaEcC45odoV0hRKGWHBMRCOccaAaw1CEW6NEGrqpiClkdzpgo3R9d7XxJBStK78ir7uXyqBlEN1JZSH6nr2as-qVNtyG7rY500lK_ICBPsDCJZrIw</recordid><startdate>20170801</startdate><enddate>20170801</enddate><creator>Javidmanesh, Elham</creator><general>ASME</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20170801</creationdate><title>Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons</title><author>Javidmanesh, Elham</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a249t-2a34196fd00559502bf89c8aac6e1f316f353315bb16a05ec66a7f7199c95fb83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Javidmanesh, Elham</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of dynamic systems, measurement, and control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Javidmanesh, Elham</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons</atitle><jtitle>Journal of dynamic systems, measurement, and control</jtitle><stitle>J. Dyn. Sys., Meas., Control</stitle><date>2017-08-01</date><risdate>2017</risdate><volume>139</volume><issue>8</issue><issn>0022-0434</issn><eissn>1528-9028</eissn><abstract>In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.</abstract><pub>ASME</pub><doi>10.1115/1.4036229</doi></addata></record>
fulltext fulltext
identifier ISSN: 0022-0434
ispartof Journal of dynamic systems, measurement, and control, 2017-08, Vol.139 (8)
issn 0022-0434
1528-9028
language eng
recordid cdi_crossref_primary_10_1115_1_4036229
source Alma/SFX Local Collection; ASME Transactions Journals (Current)
title Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T03%3A40%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-asme_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Global%20Stability%20and%20Bifurcation%20in%20Delayed%20Bidirectional%20Associative%20Memory%20Neural%20Networks%20With%20an%20Arbitrary%20Number%20of%20Neurons&rft.jtitle=Journal%20of%20dynamic%20systems,%20measurement,%20and%20control&rft.au=Javidmanesh,%20Elham&rft.date=2017-08-01&rft.volume=139&rft.issue=8&rft.issn=0022-0434&rft.eissn=1528-9028&rft_id=info:doi/10.1115/1.4036229&rft_dat=%3Casme_cross%3E384816%3C/asme_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true