Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-01

AUTHORS

Hong-Bing Zeng, Ju H. Park, Jian-Wei Xia

ABSTRACT

In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov–Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples. More... »

PAGES

83-91

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11071-014-1646-0

DOI

http://dx.doi.org/10.1007/s11071-014-1646-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011530295


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/2008/0801", 
        "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 and Information Engineering, Hunan University of Technology, 412007, Zhuzhou, China", 
            "Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, 712-749, Kyongsan, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zeng", 
        "givenName": "Hong-Bing", 
        "id": "sg:person.014560140443.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014560140443.76"
        ], 
        "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": "Liaocheng University", 
          "id": "https://www.grid.ac/institutes/grid.411351.3", 
          "name": [
            "School of Mathematic Science, Liaocheng University, 252000, Liaocheng, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xia", 
        "givenName": "Jian-Wei", 
        "id": "sg:person.015722413143.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015722413143.90"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.amc.2011.02.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001982772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2005.03.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003695671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2005.03.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003695671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-010-9807-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005906736", 
          "https://doi.org/10.1007/s11071-010-9807-2"
        ], 
        "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.amc.2013.07.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009196961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2007.08.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011177587"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2011.06.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013766938"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cnsns.2013.01.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013771427"
        ], 
        "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.2010.09.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021448796"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physleta.2010.01.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025374206"
        ], 
        "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.amc.2013.11.087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036259339"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00207179608921866", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043231379"
        ], 
        "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": "sg:pub.10.1007/s11071-011-0097-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046653755", 
          "https://doi.org/10.1007/s11071-011-0097-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-012-0350-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047212273", 
          "https://doi.org/10.1007/s11071-012-0350-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physleta.2006.01.061", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047944262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neucom.2010.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048065209"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-009-9632-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051925860", 
          "https://doi.org/10.1007/s11071-009-9632-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-009-9632-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051925860", 
          "https://doi.org/10.1007/s11071-009-9632-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.automatica.2009.03.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052690760"
        ], 
        "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/tcsii.2009.2024244", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061570100"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2006.881488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2006.888373", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2007.912593", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717344"
        ], 
        "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.2010.2056383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2111383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717854"
        ], 
        "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.2011.2147331", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2011.2169425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2011.2178326", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2011.2178563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2012.2224883", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnnls.2012.2232938", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061718213"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2005.851539", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061796517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471427950", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109491859"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471427950", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109491859"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471427950", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109491859"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-01", 
    "datePublishedReg": "2015-01-01", 
    "description": "In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov\u2013Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11071-014-1646-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7185797", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7480920", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7189903", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1040905", 
        "issn": [
          "0924-090X", 
          "1573-269X"
        ], 
        "name": "Nonlinear Dynamics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "79"
      }
    ], 
    "name": "Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties", 
    "pagination": "83-91", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "36ef8b12d0dccc8759da5a77ad0620ed90c4e28b2e957a3d786893ad8254bfbd"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11071-014-1646-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1011530295"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11071-014-1646-0", 
      "https://app.dimensions.ai/details/publication/pub.1011530295"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T23:36", 
    "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_8693_00000582.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11071-014-1646-0"
  }
]
 

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/s11071-014-1646-0'

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/s11071-014-1646-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1646-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11071-014-1646-0'


 

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

203 TRIPLES      21 PREDICATES      65 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11071-014-1646-0 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Na4f79afb90ba4d20807c8bf17c671e2e
4 schema:citation sg:pub.10.1007/s11071-009-9632-7
5 sg:pub.10.1007/s11071-010-9807-2
6 sg:pub.10.1007/s11071-011-0097-0
7 sg:pub.10.1007/s11071-012-0350-1
8 https://doi.org/10.1002/0471427950
9 https://doi.org/10.1016/j.amc.2011.02.006
10 https://doi.org/10.1016/j.amc.2013.07.017
11 https://doi.org/10.1016/j.amc.2013.11.087
12 https://doi.org/10.1016/j.automatica.2007.08.018
13 https://doi.org/10.1016/j.automatica.2009.03.004
14 https://doi.org/10.1016/j.automatica.2010.10.014
15 https://doi.org/10.1016/j.automatica.2011.06.025
16 https://doi.org/10.1016/j.automatica.2013.05.030
17 https://doi.org/10.1016/j.cnsns.2013.01.014
18 https://doi.org/10.1016/j.neucom.2010.09.020
19 https://doi.org/10.1016/j.neucom.2010.11.018
20 https://doi.org/10.1016/j.neunet.2005.03.015
21 https://doi.org/10.1016/j.neunet.2014.02.012
22 https://doi.org/10.1016/j.physleta.2006.01.061
23 https://doi.org/10.1016/j.physleta.2006.03.078
24 https://doi.org/10.1016/j.physleta.2010.01.007
25 https://doi.org/10.1080/00207179608921866
26 https://doi.org/10.1109/tcsii.2009.2015399
27 https://doi.org/10.1109/tcsii.2009.2024244
28 https://doi.org/10.1109/tnn.2006.881488
29 https://doi.org/10.1109/tnn.2006.888373
30 https://doi.org/10.1109/tnn.2007.912593
31 https://doi.org/10.1109/tnn.2009.2034742
32 https://doi.org/10.1109/tnn.2010.2056383
33 https://doi.org/10.1109/tnn.2011.2111383
34 https://doi.org/10.1109/tnn.2011.2128341
35 https://doi.org/10.1109/tnn.2011.2147331
36 https://doi.org/10.1109/tnn.2011.2169425
37 https://doi.org/10.1109/tnnls.2011.2178326
38 https://doi.org/10.1109/tnnls.2011.2178563
39 https://doi.org/10.1109/tnnls.2012.2224883
40 https://doi.org/10.1109/tnnls.2012.2232938
41 https://doi.org/10.1109/tsmcb.2005.851539
42 schema:datePublished 2015-01
43 schema:datePublishedReg 2015-01-01
44 schema:description In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov–Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree false
48 schema:isPartOf N2efbd40d1dd24b6c925d4b78ed4bb190
49 Nbfe1bf66491c4183a2185cc825679065
50 sg:journal.1040905
51 schema:name Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties
52 schema:pagination 83-91
53 schema:productId N0e84203a95eb4feea7aa0aa367585b1e
54 N8aec8d38966e4eb89800eb7c031d118a
55 Nd17dbaed9cf642cd8287513e97856470
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011530295
57 https://doi.org/10.1007/s11071-014-1646-0
58 schema:sdDatePublished 2019-04-10T23:36
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher N9c6bf8d8fbbd4d83b48549cf59fbe1be
61 schema:url http://link.springer.com/10.1007%2Fs11071-014-1646-0
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N0e84203a95eb4feea7aa0aa367585b1e schema:name doi
66 schema:value 10.1007/s11071-014-1646-0
67 rdf:type schema:PropertyValue
68 N2205840726c44ef5b26423899e4183a4 rdf:first sg:person.015722413143.90
69 rdf:rest rdf:nil
70 N2efbd40d1dd24b6c925d4b78ed4bb190 schema:issueNumber 1
71 rdf:type schema:PublicationIssue
72 N8aec8d38966e4eb89800eb7c031d118a schema:name dimensions_id
73 schema:value pub.1011530295
74 rdf:type schema:PropertyValue
75 N9c6bf8d8fbbd4d83b48549cf59fbe1be schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 Na4f79afb90ba4d20807c8bf17c671e2e rdf:first sg:person.014560140443.76
78 rdf:rest Ndd2037e4a4d34674bbee215cfce14d0d
79 Nbfe1bf66491c4183a2185cc825679065 schema:volumeNumber 79
80 rdf:type schema:PublicationVolume
81 Nd17dbaed9cf642cd8287513e97856470 schema:name readcube_id
82 schema:value 36ef8b12d0dccc8759da5a77ad0620ed90c4e28b2e957a3d786893ad8254bfbd
83 rdf:type schema:PropertyValue
84 Ndd2037e4a4d34674bbee215cfce14d0d rdf:first sg:person.07705373347.23
85 rdf:rest N2205840726c44ef5b26423899e4183a4
86 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
87 schema:name Information and Computing Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
90 schema:name Artificial Intelligence and Image Processing
91 rdf:type schema:DefinedTerm
92 sg:grant.7185797 http://pending.schema.org/fundedItem sg:pub.10.1007/s11071-014-1646-0
93 rdf:type schema:MonetaryGrant
94 sg:grant.7189903 http://pending.schema.org/fundedItem sg:pub.10.1007/s11071-014-1646-0
95 rdf:type schema:MonetaryGrant
96 sg:grant.7480920 http://pending.schema.org/fundedItem sg:pub.10.1007/s11071-014-1646-0
97 rdf:type schema:MonetaryGrant
98 sg:journal.1040905 schema:issn 0924-090X
99 1573-269X
100 schema:name Nonlinear Dynamics
101 rdf:type schema:Periodical
102 sg:person.014560140443.76 schema:affiliation https://www.grid.ac/institutes/grid.413028.c
103 schema:familyName Zeng
104 schema:givenName Hong-Bing
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014560140443.76
106 rdf:type schema:Person
107 sg:person.015722413143.90 schema:affiliation https://www.grid.ac/institutes/grid.411351.3
108 schema:familyName Xia
109 schema:givenName Jian-Wei
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015722413143.90
111 rdf:type schema:Person
112 sg:person.07705373347.23 schema:affiliation https://www.grid.ac/institutes/grid.413028.c
113 schema:familyName Park
114 schema:givenName Ju H.
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07705373347.23
116 rdf:type schema:Person
117 sg:pub.10.1007/s11071-009-9632-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051925860
118 https://doi.org/10.1007/s11071-009-9632-7
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/s11071-010-9807-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005906736
121 https://doi.org/10.1007/s11071-010-9807-2
122 rdf:type schema:CreativeWork
123 sg:pub.10.1007/s11071-011-0097-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046653755
124 https://doi.org/10.1007/s11071-011-0097-0
125 rdf:type schema:CreativeWork
126 sg:pub.10.1007/s11071-012-0350-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047212273
127 https://doi.org/10.1007/s11071-012-0350-1
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1002/0471427950 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109491859
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.amc.2011.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001982772
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.amc.2013.07.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009196961
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.amc.2013.11.087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036259339
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.automatica.2007.08.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011177587
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.automatica.2009.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052690760
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.automatica.2010.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032303085
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.automatica.2011.06.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013766938
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/j.automatica.2013.05.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044667730
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.cnsns.2013.01.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013771427
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.neucom.2010.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021448796
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.neucom.2010.11.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048065209
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.neunet.2005.03.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003695671
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.neunet.2014.02.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019063714
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.physleta.2006.01.061 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047944262
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.physleta.2006.03.078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008376384
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.physleta.2010.01.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025374206
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1080/00207179608921866 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043231379
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/tcsii.2009.2015399 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061570044
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/tcsii.2009.2024244 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061570100
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1109/tnn.2006.881488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717096
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1109/tnn.2006.888373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717144
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1109/tnn.2007.912593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717344
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/tnn.2009.2034742 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717630
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/tnn.2010.2056383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717754
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/tnn.2011.2111383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717854
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/tnn.2011.2128341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717874
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/tnn.2011.2147331 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717895
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1109/tnn.2011.2169425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717967
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/tnnls.2011.2178326 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718018
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1109/tnnls.2011.2178563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718036
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1109/tnnls.2012.2224883 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718188
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1109/tnnls.2012.2232938 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718213
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1109/tsmcb.2005.851539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061796517
196 rdf:type schema:CreativeWork
197 https://www.grid.ac/institutes/grid.411351.3 schema:alternateName Liaocheng University
198 schema:name School of Mathematic Science, Liaocheng University, 252000, Liaocheng, Shandong, China
199 rdf:type schema:Organization
200 https://www.grid.ac/institutes/grid.413028.c schema:alternateName Yeungnam University
201 schema:name Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, 712-749, Kyongsan, Republic of Korea
202 School of Electrical and Information Engineering, Hunan University of Technology, 412007, Zhuzhou, China
203 rdf:type schema:Organization
 




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


...