On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-08

AUTHORS

Yurong Liu, Zidong Wang, Xiaohui Liu

ABSTRACT

In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov–Krasovskii stability theory and the Itô differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions. More... »

PAGES

19-35

References to SciGraph publications

  • 2008-01. Adaptive output-feedback regulation for nonlinear delayed systems using neural network in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2008-10. Feedback stabilization over wireless network using adaptive coded modulation in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2007-10. Robust Control of Uncertain Stochastic Recurrent Neural Networks with Time-varying Delay in NEURAL PROCESSING LETTERS
  • 2008-04. Parametric approach for the normal Luenberger function observer design in second-order descriptor linear systems in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2008-07. Dissipativity analysis of neural networks with time-varying delays in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2008-10. Feedback scheduling of model-based networked control systems with flexible workload in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2008-10. Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • 2008-04. Intelligent control for improvements in PEM fuel cell flow performance in INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11063-009-9107-3

    DOI

    http://dx.doi.org/10.1007/s11063-009-9107-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1025221174


    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/2008/0103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Numerical and Computational Mathematics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Yangzhou University", 
              "id": "https://www.grid.ac/institutes/grid.268415.c", 
              "name": [
                "Department of Mathematics, Yangzhou University, 225002, Yangzhou, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Yurong", 
            "id": "sg:person.016202052327.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016202052327.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Brunel University London", 
              "id": "https://www.grid.ac/institutes/grid.7728.a", 
              "name": [
                "Department of Information Systems and Computing, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Zidong", 
            "id": "sg:person.0707123563.14", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707123563.14"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Brunel University London", 
              "id": "https://www.grid.ac/institutes/grid.7728.a", 
              "name": [
                "Department of Information Systems and Computing, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Xiaohui", 
            "id": "sg:person.012043417357.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012043417357.16"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "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/s11633-008-0103-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004362942", 
              "https://doi.org/10.1007/s11633-008-0103-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physa.2006.04.105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007835084"
            ], 
            "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.2006.02.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012879444"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11063-007-9045-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013814925", 
              "https://doi.org/10.1007/s11063-007-9045-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physleta.2005.07.042", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016626799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chaos.2005.08.166", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022966743"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chaos.2005.08.166", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022966743"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11633-008-0401-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023509027", 
              "https://doi.org/10.1007/s11633-008-0401-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11633-008-0389-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029258646", 
              "https://doi.org/10.1007/s11633-008-0389-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11633-008-0381-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030957731", 
              "https://doi.org/10.1007/s11633-008-0381-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2007.07.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035696102"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chaos.2005.08.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035713433"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco.1996.8.6.1135", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035846068"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11633-008-0145-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044638530", 
              "https://doi.org/10.1007/s11633-008-0145-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physleta.2004.12.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045330945"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11633-008-0290-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047234309", 
              "https://doi.org/10.1007/s11633-008-0290-x"
            ], 
            "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": "sg:pub.10.1007/s11633-008-0125-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049279572", 
              "https://doi.org/10.1007/s11633-008-0125-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0951-7715/19/7/008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059109524"
            ], 
            "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/10.1109/9.57016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061245204"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnn.2003.820839", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061716668"
            ], 
            "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.2006.872355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061717007"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnn.2007.903147", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061717281"
            ], 
            "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/cdc.2000.914233", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094583338"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611970777", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098556247"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2009-08", 
        "datePublishedReg": "2009-08-01", 
        "description": "In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov\u2013Krasovskii stability theory and the It\u00f4 differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11063-009-9107-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1132792", 
            "issn": [
              "1370-4621", 
              "1573-773X"
            ], 
            "name": "Neural Processing Letters", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "30"
          }
        ], 
        "name": "On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching", 
        "pagination": "19-35", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "78275cbb40b85346498a6024a602965bc0ec1f349fe1e88b110dd2f9d6fc6003"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11063-009-9107-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1025221174"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11063-009-9107-3", 
          "https://app.dimensions.ai/details/publication/pub.1025221174"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:26", 
        "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/v90/0000000345_0000000345/records_64115_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs11063-009-9107-3"
      }
    ]
     

    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/s11063-009-9107-3'

    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/s11063-009-9107-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11063-009-9107-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11063-009-9107-3'


     

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

    173 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11063-009-9107-3 schema:about anzsrc-for:01
    2 anzsrc-for:0103
    3 schema:author N8e671489b3dd4d36b62c4db5a6f85767
    4 schema:citation sg:pub.10.1007/s11063-007-9045-x
    5 sg:pub.10.1007/s11633-008-0103-2
    6 sg:pub.10.1007/s11633-008-0125-9
    7 sg:pub.10.1007/s11633-008-0145-5
    8 sg:pub.10.1007/s11633-008-0290-x
    9 sg:pub.10.1007/s11633-008-0381-8
    10 sg:pub.10.1007/s11633-008-0389-0
    11 sg:pub.10.1007/s11633-008-0401-8
    12 https://doi.org/10.1016/j.chaos.2005.08.004
    13 https://doi.org/10.1016/j.chaos.2005.08.166
    14 https://doi.org/10.1016/j.neucom.2007.07.003
    15 https://doi.org/10.1016/j.neunet.2005.03.015
    16 https://doi.org/10.1016/j.neunet.2006.02.006
    17 https://doi.org/10.1016/j.physa.2006.04.105
    18 https://doi.org/10.1016/j.physleta.2004.12.007
    19 https://doi.org/10.1016/j.physleta.2005.07.042
    20 https://doi.org/10.1016/j.physleta.2006.01.061
    21 https://doi.org/10.1016/j.physleta.2006.03.078
    22 https://doi.org/10.1088/0951-7715/19/7/008
    23 https://doi.org/10.1109/72.329700
    24 https://doi.org/10.1109/9.57016
    25 https://doi.org/10.1109/cdc.2000.914233
    26 https://doi.org/10.1109/tnn.2003.820839
    27 https://doi.org/10.1109/tnn.2004.841813
    28 https://doi.org/10.1109/tnn.2006.872355
    29 https://doi.org/10.1109/tnn.2007.903147
    30 https://doi.org/10.1109/tnn.2007.912593
    31 https://doi.org/10.1137/1.9781611970777
    32 https://doi.org/10.1162/neco.1996.8.6.1135
    33 schema:datePublished 2009-08
    34 schema:datePublishedReg 2009-08-01
    35 schema:description In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov–Krasovskii stability theory and the Itô differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.
    36 schema:genre research_article
    37 schema:inLanguage en
    38 schema:isAccessibleForFree false
    39 schema:isPartOf N3e742e0d3fc94a978df18d3c24f6eef5
    40 Ne0ba8ddee05645d89d9eb49738230e06
    41 sg:journal.1132792
    42 schema:name On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching
    43 schema:pagination 19-35
    44 schema:productId N43bbc5c7f5524c4a99ea667f41103df5
    45 N639480258d5544ada19712e0142536d9
    46 Ne3ed57e2fa3d4926928ba8bb33f1a7a4
    47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025221174
    48 https://doi.org/10.1007/s11063-009-9107-3
    49 schema:sdDatePublished 2019-04-11T09:26
    50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    51 schema:sdPublisher N4d86f3aa72454aca8b1cce8a3e038580
    52 schema:url http://link.springer.com/10.1007%2Fs11063-009-9107-3
    53 sgo:license sg:explorer/license/
    54 sgo:sdDataset articles
    55 rdf:type schema:ScholarlyArticle
    56 N088d47a940bd4572aa6891bd9eb13ef1 rdf:first sg:person.0707123563.14
    57 rdf:rest N26e5dd59a750438ca806e549f545c497
    58 N26e5dd59a750438ca806e549f545c497 rdf:first sg:person.012043417357.16
    59 rdf:rest rdf:nil
    60 N3e742e0d3fc94a978df18d3c24f6eef5 schema:issueNumber 1
    61 rdf:type schema:PublicationIssue
    62 N43bbc5c7f5524c4a99ea667f41103df5 schema:name dimensions_id
    63 schema:value pub.1025221174
    64 rdf:type schema:PropertyValue
    65 N4d86f3aa72454aca8b1cce8a3e038580 schema:name Springer Nature - SN SciGraph project
    66 rdf:type schema:Organization
    67 N639480258d5544ada19712e0142536d9 schema:name readcube_id
    68 schema:value 78275cbb40b85346498a6024a602965bc0ec1f349fe1e88b110dd2f9d6fc6003
    69 rdf:type schema:PropertyValue
    70 N8e671489b3dd4d36b62c4db5a6f85767 rdf:first sg:person.016202052327.40
    71 rdf:rest N088d47a940bd4572aa6891bd9eb13ef1
    72 Ne0ba8ddee05645d89d9eb49738230e06 schema:volumeNumber 30
    73 rdf:type schema:PublicationVolume
    74 Ne3ed57e2fa3d4926928ba8bb33f1a7a4 schema:name doi
    75 schema:value 10.1007/s11063-009-9107-3
    76 rdf:type schema:PropertyValue
    77 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    78 schema:name Mathematical Sciences
    79 rdf:type schema:DefinedTerm
    80 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
    81 schema:name Numerical and Computational Mathematics
    82 rdf:type schema:DefinedTerm
    83 sg:journal.1132792 schema:issn 1370-4621
    84 1573-773X
    85 schema:name Neural Processing Letters
    86 rdf:type schema:Periodical
    87 sg:person.012043417357.16 schema:affiliation https://www.grid.ac/institutes/grid.7728.a
    88 schema:familyName Liu
    89 schema:givenName Xiaohui
    90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012043417357.16
    91 rdf:type schema:Person
    92 sg:person.016202052327.40 schema:affiliation https://www.grid.ac/institutes/grid.268415.c
    93 schema:familyName Liu
    94 schema:givenName Yurong
    95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016202052327.40
    96 rdf:type schema:Person
    97 sg:person.0707123563.14 schema:affiliation https://www.grid.ac/institutes/grid.7728.a
    98 schema:familyName Wang
    99 schema:givenName Zidong
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707123563.14
    101 rdf:type schema:Person
    102 sg:pub.10.1007/s11063-007-9045-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1013814925
    103 https://doi.org/10.1007/s11063-007-9045-x
    104 rdf:type schema:CreativeWork
    105 sg:pub.10.1007/s11633-008-0103-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004362942
    106 https://doi.org/10.1007/s11633-008-0103-2
    107 rdf:type schema:CreativeWork
    108 sg:pub.10.1007/s11633-008-0125-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049279572
    109 https://doi.org/10.1007/s11633-008-0125-9
    110 rdf:type schema:CreativeWork
    111 sg:pub.10.1007/s11633-008-0145-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044638530
    112 https://doi.org/10.1007/s11633-008-0145-5
    113 rdf:type schema:CreativeWork
    114 sg:pub.10.1007/s11633-008-0290-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1047234309
    115 https://doi.org/10.1007/s11633-008-0290-x
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/s11633-008-0381-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030957731
    118 https://doi.org/10.1007/s11633-008-0381-8
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/s11633-008-0389-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029258646
    121 https://doi.org/10.1007/s11633-008-0389-0
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s11633-008-0401-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023509027
    124 https://doi.org/10.1007/s11633-008-0401-8
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1016/j.chaos.2005.08.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035713433
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/j.chaos.2005.08.166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022966743
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/j.neucom.2007.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035696102
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/j.neunet.2005.03.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003695671
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.neunet.2006.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012879444
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/j.physa.2006.04.105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007835084
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1016/j.physleta.2004.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045330945
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1016/j.physleta.2005.07.042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016626799
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/j.physleta.2006.01.061 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047944262
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/j.physleta.2006.03.078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008376384
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1088/0951-7715/19/7/008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059109524
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1109/72.329700 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218518
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1109/9.57016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061245204
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1109/cdc.2000.914233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094583338
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1109/tnn.2003.820839 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061716668
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1109/tnn.2004.841813 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061716829
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1109/tnn.2006.872355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717007
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1109/tnn.2007.903147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717281
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1109/tnn.2007.912593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717344
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1137/1.9781611970777 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098556247
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1162/neco.1996.8.6.1135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035846068
    167 rdf:type schema:CreativeWork
    168 https://www.grid.ac/institutes/grid.268415.c schema:alternateName Yangzhou University
    169 schema:name Department of Mathematics, Yangzhou University, 225002, Yangzhou, People’s Republic of China
    170 rdf:type schema:Organization
    171 https://www.grid.ac/institutes/grid.7728.a schema:alternateName Brunel University London
    172 schema:name Department of Information Systems and Computing, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK
    173 rdf:type schema:Organization
     




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


    ...