Ontology type: schema:ScholarlyArticle
2014-09
AUTHORSHao Shen, Ju H. Park, Zheng-Guang Wu
ABSTRACTThis paper is concerned with the problem of finite-time synchronization control for uncertain Markov jump neural networks in the presence of constraints on the control input amplitude. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. By using a parameter-dependent Lyapunov functional and a simple matrix decoupling method, a sufficient condition is proposed to ensure that the considered networks are stochastically synchronized over a finite-time interval. The desired mode-independent controller parameters can be computed via solving a convex optimization problem. Finally, two chaos neural networks are employed to demonstrate the effectiveness of our proposed approach. More... »
PAGES1709-1720
http://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3
DOIhttp://dx.doi.org/10.1007/s11071-014-1412-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1006452208
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Yeungnam University",
"id": "https://www.grid.ac/institutes/grid.413028.c",
"name": [
"School of Electrical Engineering and Information, Anhui University of Technology, 243002, Ma\u2019anshan, China",
"Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, 712-749, Kyongsan, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Shen",
"givenName": "Hao",
"id": "sg:person.016211042755.91",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016211042755.91"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Yeungnam University",
"id": "https://www.grid.ac/institutes/grid.413028.c",
"name": [
"Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, 712-749, Kyongsan, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Park",
"givenName": "Ju H.",
"id": "sg:person.07705373347.23",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07705373347.23"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Zhejiang University",
"id": "https://www.grid.ac/institutes/grid.13402.34",
"name": [
"National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, 310027, Hangzhou, Zhejiang, China"
],
"type": "Organization"
},
"familyName": "Wu",
"givenName": "Zheng-Guang",
"id": "sg:person.010337770007.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010337770007.21"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1080/00207721003790351",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000578669"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-012-0500-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000741175",
"https://doi.org/10.1007/s11071-012-0500-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-013-0942-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001557420",
"https://doi.org/10.1007/s11071-013-0942-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1162/neco.2007.19.8.2149",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005235775"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.automatica.2010.06.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007398852"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.physleta.2006.03.078",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008376384"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2011.06.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010021041"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.automatica.2012.05.024",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010583945"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neucom.2007.07.016",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011298613"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jfranklin.2011.11.011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016216678"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.automatica.2010.06.038",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022094711"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2013.04.014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023505626"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2004.02.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024542263"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-011-0278-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024771465",
"https://doi.org/10.1007/s11071-011-0278-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-011-0278-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024771465",
"https://doi.org/10.1007/s11071-011-0278-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2012.08.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025022091"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cnsns.2011.03.028",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026491038"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-011-0138-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027578258",
"https://doi.org/10.1007/s11071-011-0138-8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.automatica.2013.09.041",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035849481"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11571-011-9163-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036265013",
"https://doi.org/10.1007/s11571-011-9163-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11071-011-0029-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036836317",
"https://doi.org/10.1007/s11071-011-0029-z"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2011.08.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041187455"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.sysconle.2007.12.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048207816"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/00207179.2013.878478",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049203647"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.eswa.2011.07.038",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052368468"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1049/iet-cta.2012.0822",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1056823641"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tac.2010.2046607",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061477593"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcsi.2010.2097691",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061567016"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcsi.2013.2246213",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061567643"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcsii.2005.850413",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061569111"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tcsii.2009.2015399",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061570044"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnn.2007.910738",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061717317"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnn.2009.2015085",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061717534"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnn.2010.2050781",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061717740"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnn.2010.2054108",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061717751"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnn.2011.2163203",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061717933"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnnls.2011.2177671",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061718006"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnnls.2012.2202246",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061718127"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tsmcb.2008.2002812",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061796851"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tsmcb.2012.2199751",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061797502"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tsmcb.2012.2230441",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061797610"
],
"type": "CreativeWork"
}
],
"datePublished": "2014-09",
"datePublishedReg": "2014-09-01",
"description": "This paper is concerned with the problem of finite-time synchronization control for uncertain Markov jump neural networks in the presence of constraints on the control input amplitude. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. By using a parameter-dependent Lyapunov functional and a simple matrix decoupling method, a sufficient condition is proposed to ensure that the considered networks are stochastically synchronized over a finite-time interval. The desired mode-independent controller parameters can be computed via solving a convex optimization problem. Finally, two chaos neural networks are employed to demonstrate the effectiveness of our proposed approach.",
"genre": "research_article",
"id": "sg:pub.10.1007/s11071-014-1412-3",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.7176726",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7480920",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7013368",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7208315",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1040905",
"issn": [
"0924-090X",
"1573-269X"
],
"name": "Nonlinear Dynamics",
"type": "Periodical"
},
{
"issueNumber": "4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "77"
}
],
"name": "Finite-time synchronization control for uncertain Markov jump neural networks with input constraints",
"pagination": "1709-1720",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"1ebbd4babb6cc8189582486142dcec7e0619efd148a3f6449d0de6b61a29b3fb"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11071-014-1412-3"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1006452208"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11071-014-1412-3",
"https://app.dimensions.ai/details/publication/pub.1006452208"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T14:09",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000510.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs11071-014-1412-3"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1412-3'
This table displays all metadata directly associated to this object as RDF triples.
213 TRIPLES
21 PREDICATES
67 URIs
19 LITERALS
7 BLANK NODES