Improved Results on Guaranteed Generalized H2 Performance State Estimation for Delayed Static Neural Networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-08

AUTHORS

Yanjun Shu, Xin-Ge Liu, Yajuan Liu, Ju H. Park

ABSTRACT

This paper is concerned with the guaranteed generalized H2 performance state estimation for a class of static neural networks with a time-varying delay. A more general Arcak-type state estimator rather than the Luenberger-type state estimator is adopted to deal with this problem. Based on the Lyapunov stability theory, the inequality techniques and the delay-partitioning approach, some novel delay-dependent design criteria in terms of linear matrix inequalities (LMIs) are proposed ensuring that the resulting error system is globally asymptotically stable and a prescribed generalized H2 performance is guaranteed. The estimator gain matrices can be derived by solving the LMIs. Compared with the existing results, the sufficient conditions presented in this paper are with less conservatism. Numerical examples are given to illustrate the effectiveness and superiority of the developed method over the existing approaches. A comparison between the Arcak-type state estimator and Luenberger-type state estimator is given simultaneously. More... »

PAGES

3114-3142

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00034-016-0463-8

DOI

http://dx.doi.org/10.1007/s00034-016-0463-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021465685


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/1005", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Communications Technologies", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/10", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Technology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Central South University", 
          "id": "https://www.grid.ac/institutes/grid.216417.7", 
          "name": [
            "School of Mathematics and Statistics, Central South University, 410083, Changsha, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shu", 
        "givenName": "Yanjun", 
        "id": "sg:person.011264343651.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011264343651.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Central South University", 
          "id": "https://www.grid.ac/institutes/grid.216417.7", 
          "name": [
            "School of Mathematics and Statistics, Central South University, 410083, Changsha, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Xin-Ge", 
        "id": "sg:person.014274313762.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014274313762.73"
        ], 
        "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, 38541, Kyongsan, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Yajuan", 
        "id": "sg:person.010272216655.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010272216655.66"
        ], 
        "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, 38541, 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"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11071-011-0286-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001341964", 
          "https://doi.org/10.1007/s11071-011-0286-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/rnc.3243", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002041154"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2015.07.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004457959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2014.11.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005289779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jfranklin.2015.01.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005520448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.isatra.2015.10.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006695089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-013-1531-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010997856", 
          "https://doi.org/10.1007/s00521-013-1531-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2016.02.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011275095"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2012.05.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012602699"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jfranklin.2015.08.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014153641"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2014.10.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016155059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2014.02.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019063714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2013.09.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020370107"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2010.09.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021448796"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0005-1098(01)00160-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028280850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0893-6080(03)00192-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031729841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0893-6080(03)00192-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031729841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2015.07.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031962113"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.isatra.2015.09.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032293858"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2010.10.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032303085"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sysconle.2016.03.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034550018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2015.07.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034818693"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00034-014-9814-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036618263", 
          "https://doi.org/10.1007/s00034-014-9814-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2014.12.062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043299600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2010.09.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043525195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2013.05.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044667730"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sysconle.2013.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046234708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.81.10.3088", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049596495"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-012-1061-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050937880", 
          "https://doi.org/10.1007/s00521-012-1061-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-011-0010-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051594974", 
          "https://doi.org/10.1007/s11071-011-0010-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amc.2013.10.075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052591459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/iet-cta.2013.0400", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056823772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1049/iet-cta.2014.0962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056824171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.329700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tac.2013.2289706", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061478928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tac.2015.2404271", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061479459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tac.2015.2503047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061479847"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsi.2008.2003372", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061566134"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsii.2012.2234930", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061570751"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsii.2013.2258258", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061570810"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcst.2010.2042296", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061572902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tfuzz.2012.2187299", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061606563"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2004.841813", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061716829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2007.908633", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2007.912319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2009.2034742", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2010.2054107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717750"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2128341", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2131679", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2147331", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2013.2251000", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718281"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2014.2317880", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2014.2334511", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2014.2387434", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718753"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2014.2387885", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2015.2449898", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2010.2103068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061802534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0217984909017807", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062944271"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-08", 
    "datePublishedReg": "2017-08-01", 
    "description": "This paper is concerned with the guaranteed generalized H2 performance state estimation for a class of static neural networks with a time-varying delay. A more general Arcak-type state estimator rather than the Luenberger-type state estimator is adopted to deal with this problem. Based on the Lyapunov stability theory, the inequality techniques and the delay-partitioning approach, some novel delay-dependent design criteria in terms of linear matrix inequalities (LMIs) are proposed ensuring that the resulting error system is globally asymptotically stable and a prescribed generalized H2 performance is guaranteed. The estimator gain matrices can be derived by solving the LMIs. Compared with the existing results, the sufficient conditions presented in this paper are with less conservatism. Numerical examples are given to illustrate the effectiveness and superiority of the developed method over the existing approaches. A comparison between the Arcak-type state estimator and Luenberger-type state estimator is given simultaneously.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00034-016-0463-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7175555", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1136068", 
        "issn": [
          "0278-081X", 
          "1531-5878"
        ], 
        "name": "Circuits, Systems, and Signal Processing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "36"
      }
    ], 
    "name": "Improved Results on Guaranteed Generalized H2 Performance State Estimation for Delayed Static Neural Networks", 
    "pagination": "3114-3142", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6645cce38658c9ae825fc6e9eed246b64640a282954c2aa4e5b3b42ee4dc87c0"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00034-016-0463-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1021465685"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00034-016-0463-8", 
      "https://app.dimensions.ai/details/publication/pub.1021465685"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:22", 
    "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/v90/0000000362_0000000362/records_87083_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00034-016-0463-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s00034-016-0463-8'

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/s00034-016-0463-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00034-016-0463-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00034-016-0463-8'


 

This table displays all metadata directly associated to this object as RDF triples.

263 TRIPLES      21 PREDICATES      84 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00034-016-0463-8 schema:about anzsrc-for:10
2 anzsrc-for:1005
3 schema:author Ncdc6eb5b38ef4fa6ade3ce7b3c5f01ef
4 schema:citation sg:pub.10.1007/s00034-014-9814-5
5 sg:pub.10.1007/s00521-012-1061-8
6 sg:pub.10.1007/s00521-013-1531-7
7 sg:pub.10.1007/s11071-011-0010-x
8 sg:pub.10.1007/s11071-011-0286-x
9 https://doi.org/10.1002/rnc.3243
10 https://doi.org/10.1016/j.amc.2013.10.075
11 https://doi.org/10.1016/j.automatica.2010.10.014
12 https://doi.org/10.1016/j.automatica.2013.05.030
13 https://doi.org/10.1016/j.automatica.2014.11.019
14 https://doi.org/10.1016/j.automatica.2015.07.017
15 https://doi.org/10.1016/j.automatica.2015.07.022
16 https://doi.org/10.1016/j.isatra.2015.09.008
17 https://doi.org/10.1016/j.isatra.2015.10.001
18 https://doi.org/10.1016/j.jfranklin.2015.01.004
19 https://doi.org/10.1016/j.jfranklin.2015.08.024
20 https://doi.org/10.1016/j.neucom.2010.09.017
21 https://doi.org/10.1016/j.neucom.2010.09.020
22 https://doi.org/10.1016/j.neucom.2012.05.021
23 https://doi.org/10.1016/j.neucom.2013.09.020
24 https://doi.org/10.1016/j.neucom.2014.10.023
25 https://doi.org/10.1016/j.neucom.2014.12.062
26 https://doi.org/10.1016/j.neucom.2015.07.038
27 https://doi.org/10.1016/j.neunet.2014.02.012
28 https://doi.org/10.1016/j.neunet.2016.02.002
29 https://doi.org/10.1016/j.sysconle.2013.07.003
30 https://doi.org/10.1016/j.sysconle.2016.03.002
31 https://doi.org/10.1016/s0005-1098(01)00160-1
32 https://doi.org/10.1016/s0893-6080(03)00192-8
33 https://doi.org/10.1049/iet-cta.2013.0400
34 https://doi.org/10.1049/iet-cta.2014.0962
35 https://doi.org/10.1073/pnas.81.10.3088
36 https://doi.org/10.1109/72.329700
37 https://doi.org/10.1109/tac.2013.2289706
38 https://doi.org/10.1109/tac.2015.2404271
39 https://doi.org/10.1109/tac.2015.2503047
40 https://doi.org/10.1109/tcsi.2008.2003372
41 https://doi.org/10.1109/tcsii.2012.2234930
42 https://doi.org/10.1109/tcsii.2013.2258258
43 https://doi.org/10.1109/tcst.2010.2042296
44 https://doi.org/10.1109/tfuzz.2012.2187299
45 https://doi.org/10.1109/tnn.2004.841813
46 https://doi.org/10.1109/tnn.2007.908633
47 https://doi.org/10.1109/tnn.2007.912319
48 https://doi.org/10.1109/tnn.2009.2034742
49 https://doi.org/10.1109/tnn.2010.2054107
50 https://doi.org/10.1109/tnn.2011.2128341
51 https://doi.org/10.1109/tnn.2011.2131679
52 https://doi.org/10.1109/tnn.2011.2147331
53 https://doi.org/10.1109/tnnls.2013.2251000
54 https://doi.org/10.1109/tnnls.2014.2317880
55 https://doi.org/10.1109/tnnls.2014.2334511
56 https://doi.org/10.1109/tnnls.2014.2387434
57 https://doi.org/10.1109/tnnls.2014.2387885
58 https://doi.org/10.1109/tnnls.2015.2449898
59 https://doi.org/10.1109/tsp.2010.2103068
60 https://doi.org/10.1142/s0217984909017807
61 schema:datePublished 2017-08
62 schema:datePublishedReg 2017-08-01
63 schema:description This paper is concerned with the guaranteed generalized H2 performance state estimation for a class of static neural networks with a time-varying delay. A more general Arcak-type state estimator rather than the Luenberger-type state estimator is adopted to deal with this problem. Based on the Lyapunov stability theory, the inequality techniques and the delay-partitioning approach, some novel delay-dependent design criteria in terms of linear matrix inequalities (LMIs) are proposed ensuring that the resulting error system is globally asymptotically stable and a prescribed generalized H2 performance is guaranteed. The estimator gain matrices can be derived by solving the LMIs. Compared with the existing results, the sufficient conditions presented in this paper are with less conservatism. Numerical examples are given to illustrate the effectiveness and superiority of the developed method over the existing approaches. A comparison between the Arcak-type state estimator and Luenberger-type state estimator is given simultaneously.
64 schema:genre research_article
65 schema:inLanguage en
66 schema:isAccessibleForFree false
67 schema:isPartOf N78f40a3525e64165973f09f09f04252f
68 Nc1d4ccf6ecf34b3b8298b8ed0b1d88ff
69 sg:journal.1136068
70 schema:name Improved Results on Guaranteed Generalized H2 Performance State Estimation for Delayed Static Neural Networks
71 schema:pagination 3114-3142
72 schema:productId N25b5ab1b1ab74f66ae06e9f4b3e9cdca
73 N368bf3ce36064050b33d516599975d99
74 Nefd8007125e946f88fadfb9212b40fdb
75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021465685
76 https://doi.org/10.1007/s00034-016-0463-8
77 schema:sdDatePublished 2019-04-11T12:22
78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
79 schema:sdPublisher N78e0015c9f8541bcab7d36f6e6fcb8ed
80 schema:url https://link.springer.com/10.1007%2Fs00034-016-0463-8
81 sgo:license sg:explorer/license/
82 sgo:sdDataset articles
83 rdf:type schema:ScholarlyArticle
84 N25b5ab1b1ab74f66ae06e9f4b3e9cdca schema:name readcube_id
85 schema:value 6645cce38658c9ae825fc6e9eed246b64640a282954c2aa4e5b3b42ee4dc87c0
86 rdf:type schema:PropertyValue
87 N368bf3ce36064050b33d516599975d99 schema:name doi
88 schema:value 10.1007/s00034-016-0463-8
89 rdf:type schema:PropertyValue
90 N71c970d53c714effb2424ce61da24460 rdf:first sg:person.010272216655.66
91 rdf:rest Ncbac7598400c40bdbab93e39225e9dc7
92 N78e0015c9f8541bcab7d36f6e6fcb8ed schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N78f40a3525e64165973f09f09f04252f schema:volumeNumber 36
95 rdf:type schema:PublicationVolume
96 Nc1d4ccf6ecf34b3b8298b8ed0b1d88ff schema:issueNumber 8
97 rdf:type schema:PublicationIssue
98 Nc91a799649ca475ab6e1146db4857919 rdf:first sg:person.014274313762.73
99 rdf:rest N71c970d53c714effb2424ce61da24460
100 Ncbac7598400c40bdbab93e39225e9dc7 rdf:first sg:person.07705373347.23
101 rdf:rest rdf:nil
102 Ncdc6eb5b38ef4fa6ade3ce7b3c5f01ef rdf:first sg:person.011264343651.18
103 rdf:rest Nc91a799649ca475ab6e1146db4857919
104 Nefd8007125e946f88fadfb9212b40fdb schema:name dimensions_id
105 schema:value pub.1021465685
106 rdf:type schema:PropertyValue
107 anzsrc-for:10 schema:inDefinedTermSet anzsrc-for:
108 schema:name Technology
109 rdf:type schema:DefinedTerm
110 anzsrc-for:1005 schema:inDefinedTermSet anzsrc-for:
111 schema:name Communications Technologies
112 rdf:type schema:DefinedTerm
113 sg:grant.7175555 http://pending.schema.org/fundedItem sg:pub.10.1007/s00034-016-0463-8
114 rdf:type schema:MonetaryGrant
115 sg:journal.1136068 schema:issn 0278-081X
116 1531-5878
117 schema:name Circuits, Systems, and Signal Processing
118 rdf:type schema:Periodical
119 sg:person.010272216655.66 schema:affiliation https://www.grid.ac/institutes/grid.413028.c
120 schema:familyName Liu
121 schema:givenName Yajuan
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010272216655.66
123 rdf:type schema:Person
124 sg:person.011264343651.18 schema:affiliation https://www.grid.ac/institutes/grid.216417.7
125 schema:familyName Shu
126 schema:givenName Yanjun
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011264343651.18
128 rdf:type schema:Person
129 sg:person.014274313762.73 schema:affiliation https://www.grid.ac/institutes/grid.216417.7
130 schema:familyName Liu
131 schema:givenName Xin-Ge
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014274313762.73
133 rdf:type schema:Person
134 sg:person.07705373347.23 schema:affiliation https://www.grid.ac/institutes/grid.413028.c
135 schema:familyName Park
136 schema:givenName Ju H.
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07705373347.23
138 rdf:type schema:Person
139 sg:pub.10.1007/s00034-014-9814-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036618263
140 https://doi.org/10.1007/s00034-014-9814-5
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s00521-012-1061-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050937880
143 https://doi.org/10.1007/s00521-012-1061-8
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s00521-013-1531-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010997856
146 https://doi.org/10.1007/s00521-013-1531-7
147 rdf:type schema:CreativeWork
148 sg:pub.10.1007/s11071-011-0010-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051594974
149 https://doi.org/10.1007/s11071-011-0010-x
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/s11071-011-0286-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1001341964
152 https://doi.org/10.1007/s11071-011-0286-x
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1002/rnc.3243 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002041154
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.amc.2013.10.075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052591459
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.automatica.2010.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032303085
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.automatica.2013.05.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044667730
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.automatica.2014.11.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005289779
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.automatica.2015.07.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004457959
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.automatica.2015.07.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034818693
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.isatra.2015.09.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032293858
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.isatra.2015.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006695089
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.jfranklin.2015.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005520448
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.jfranklin.2015.08.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014153641
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.neucom.2010.09.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043525195
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1016/j.neucom.2010.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021448796
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1016/j.neucom.2012.05.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012602699
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1016/j.neucom.2013.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020370107
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1016/j.neucom.2014.10.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016155059
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1016/j.neucom.2014.12.062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043299600
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1016/j.neucom.2015.07.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031962113
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/j.neunet.2014.02.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019063714
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/j.neunet.2016.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011275095
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/j.sysconle.2013.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046234708
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/j.sysconle.2016.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034550018
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1016/s0005-1098(01)00160-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028280850
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1016/s0893-6080(03)00192-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031729841
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1049/iet-cta.2013.0400 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056823772
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1049/iet-cta.2014.0962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056824171
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1073/pnas.81.10.3088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049596495
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1109/72.329700 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218518
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1109/tac.2013.2289706 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061478928
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1109/tac.2015.2404271 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061479459
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1109/tac.2015.2503047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061479847
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1109/tcsi.2008.2003372 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061566134
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1109/tcsii.2012.2234930 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061570751
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1109/tcsii.2013.2258258 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061570810
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1109/tcst.2010.2042296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061572902
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1109/tfuzz.2012.2187299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061606563
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1109/tnn.2004.841813 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061716829
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1109/tnn.2007.908633 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717299
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1109/tnn.2007.912319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717337
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1109/tnn.2009.2034742 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717630
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1109/tnn.2010.2054107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717750
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1109/tnn.2011.2128341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717874
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1109/tnn.2011.2131679 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717880
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1109/tnn.2011.2147331 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717895
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1109/tnnls.2013.2251000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718281
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1109/tnnls.2014.2317880 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718578
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1109/tnnls.2014.2334511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718623
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1109/tnnls.2014.2387434 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718753
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1109/tnnls.2014.2387885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718757
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1109/tnnls.2015.2449898 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718902
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1109/tsp.2010.2103068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061802534
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1142/s0217984909017807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062944271
257 rdf:type schema:CreativeWork
258 https://www.grid.ac/institutes/grid.216417.7 schema:alternateName Central South University
259 schema:name School of Mathematics and Statistics, Central South University, 410083, Changsha, China
260 rdf:type schema:Organization
261 https://www.grid.ac/institutes/grid.413028.c schema:alternateName Yeungnam University
262 schema:name Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, 38541, Kyongsan, Republic of Korea
263 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...