New stability criteria for recurrent neural networks with a time-varying delay View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-02

AUTHORS

Hong-Bing Zeng, Shen-Ping Xiao, Bin Liu

ABSTRACT

This paper deals with the stability of static recurrent neural networks (RNNs) with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the time-varying delay, its upper bound and their difference, is taken into account, and novel bounding techniques for 1 − (t) are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods. More... »

PAGES

128-133

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11633-010-0564-y

DOI

http://dx.doi.org/10.1007/s11633-010-0564-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027251551


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": "Central South University", 
          "id": "https://www.grid.ac/institutes/grid.216417.7", 
          "name": [
            "School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC", 
            "School of Information Science and Engineering, Central South University, 410083, Changsha, PRC"
          ], 
          "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": "Hunan University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.411431.2", 
          "name": [
            "School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xiao", 
        "givenName": "Shen-Ping", 
        "id": "sg:person.07572434757.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07572434757.64"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Australian National University", 
          "id": "https://www.grid.ac/institutes/grid.1001.0", 
          "name": [
            "School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC", 
            "School of Engineering, Australian National University, 0200, Canberra, ACT, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Bin", 
        "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": "https://doi.org/10.1002/rnc.1039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019995988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/rnc.1039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019995988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.chaos.2005.04.120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020287010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11633-009-0415-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029197096", 
          "https://doi.org/10.1007/s11633-009-0415-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11633-009-0415-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029197096", 
          "https://doi.org/10.1007/s11633-009-0415-x"
        ], 
        "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.physleta.2008.11.048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033299909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11633-009-0223-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036451184", 
          "https://doi.org/10.1007/s11633-009-0223-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11633-009-0223-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036451184", 
          "https://doi.org/10.1007/s11633-009-0223-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physleta.2006.10.073", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039927934"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11633-010-0199-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048790813", 
          "https://doi.org/10.1007/s11633-010-0199-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physleta.2004.03.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049866761"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.nonrwa.2008.03.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052511895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physleta.2008.12.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052941985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.265960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.363441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsi.2003.817760", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061564998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsi.2004.841574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061565309"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsi.2006.883159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061565743"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsii.2008.921597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061569950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2006.873283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717017"
        ], 
        "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.2007.903147", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717281"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2008.2001265", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717409"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2009.2014160", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2003.821455", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061796274"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-02", 
    "datePublishedReg": "2011-02-01", 
    "description": "This paper deals with the stability of static recurrent neural networks (RNNs) with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the time-varying delay, its upper bound and their difference, is taken into account, and novel bounding techniques for 1 \u2212 (t) are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11633-010-0564-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136120", 
        "issn": [
          "1476-8186", 
          "1751-8520"
        ], 
        "name": "International Journal of Automation and Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "name": "New stability criteria for recurrent neural networks with a time-varying delay", 
    "pagination": "128-133", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "81d0e945acf3c8f338218ae37df04006c873cad1dff35d035e59163ab779d0cd"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11633-010-0564-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027251551"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11633-010-0564-y", 
      "https://app.dimensions.ai/details/publication/pub.1027251551"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:49", 
    "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_8684_00000522.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11633-010-0564-y"
  }
]
 

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/s11633-010-0564-y'

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/s11633-010-0564-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11633-010-0564-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11633-010-0564-y'


 

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

157 TRIPLES      21 PREDICATES      51 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11633-010-0564-y schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Ne7044efc0bfd4925beba7697f6526132
4 schema:citation sg:pub.10.1007/s11633-009-0223-3
5 sg:pub.10.1007/s11633-009-0415-x
6 sg:pub.10.1007/s11633-010-0199-z
7 https://doi.org/10.1002/rnc.1039
8 https://doi.org/10.1016/j.chaos.2005.04.120
9 https://doi.org/10.1016/j.neunet.2005.03.015
10 https://doi.org/10.1016/j.nonrwa.2008.03.004
11 https://doi.org/10.1016/j.physleta.2004.03.038
12 https://doi.org/10.1016/j.physleta.2006.10.073
13 https://doi.org/10.1016/j.physleta.2008.11.048
14 https://doi.org/10.1016/j.physleta.2008.12.005
15 https://doi.org/10.1016/s0893-6080(03)00192-8
16 https://doi.org/10.1109/72.265960
17 https://doi.org/10.1109/72.363441
18 https://doi.org/10.1109/tcsi.2003.817760
19 https://doi.org/10.1109/tcsi.2004.841574
20 https://doi.org/10.1109/tcsi.2006.883159
21 https://doi.org/10.1109/tcsii.2008.921597
22 https://doi.org/10.1109/tnn.2006.873283
23 https://doi.org/10.1109/tnn.2006.888373
24 https://doi.org/10.1109/tnn.2007.903147
25 https://doi.org/10.1109/tnn.2008.2001265
26 https://doi.org/10.1109/tnn.2009.2014160
27 https://doi.org/10.1109/tsmcb.2003.821455
28 schema:datePublished 2011-02
29 schema:datePublishedReg 2011-02-01
30 schema:description This paper deals with the stability of static recurrent neural networks (RNNs) with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the time-varying delay, its upper bound and their difference, is taken into account, and novel bounding techniques for 1 − (t) are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.
31 schema:genre research_article
32 schema:inLanguage en
33 schema:isAccessibleForFree false
34 schema:isPartOf N268cf56be9b74ed0a1098d5ec53021e8
35 Ne4e1f2417a594309885efa7e1fc1cc6d
36 sg:journal.1136120
37 schema:name New stability criteria for recurrent neural networks with a time-varying delay
38 schema:pagination 128-133
39 schema:productId N15a5c5fd3c124561803634ab569428fd
40 N3a875acc9a8e457c960c83585c4579fc
41 N4feaa770967d4f6d9c8f54ed79908b82
42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027251551
43 https://doi.org/10.1007/s11633-010-0564-y
44 schema:sdDatePublished 2019-04-10T20:49
45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
46 schema:sdPublisher Nfe9556faed554f19bd2877f5ad1fe051
47 schema:url http://link.springer.com/10.1007%2Fs11633-010-0564-y
48 sgo:license sg:explorer/license/
49 sgo:sdDataset articles
50 rdf:type schema:ScholarlyArticle
51 N15a5c5fd3c124561803634ab569428fd schema:name doi
52 schema:value 10.1007/s11633-010-0564-y
53 rdf:type schema:PropertyValue
54 N268cf56be9b74ed0a1098d5ec53021e8 schema:issueNumber 1
55 rdf:type schema:PublicationIssue
56 N3972b0287f78418bae3d99203db4f71e rdf:first sg:person.07572434757.64
57 rdf:rest N56f4e4bc7e0c458bbfe6ac0160c144dd
58 N3a875acc9a8e457c960c83585c4579fc schema:name dimensions_id
59 schema:value pub.1027251551
60 rdf:type schema:PropertyValue
61 N48953bec545c4cf99d07cd2c3d7a00b9 schema:affiliation https://www.grid.ac/institutes/grid.1001.0
62 schema:familyName Liu
63 schema:givenName Bin
64 rdf:type schema:Person
65 N4feaa770967d4f6d9c8f54ed79908b82 schema:name readcube_id
66 schema:value 81d0e945acf3c8f338218ae37df04006c873cad1dff35d035e59163ab779d0cd
67 rdf:type schema:PropertyValue
68 N56f4e4bc7e0c458bbfe6ac0160c144dd rdf:first N48953bec545c4cf99d07cd2c3d7a00b9
69 rdf:rest rdf:nil
70 Ne4e1f2417a594309885efa7e1fc1cc6d schema:volumeNumber 8
71 rdf:type schema:PublicationVolume
72 Ne7044efc0bfd4925beba7697f6526132 rdf:first sg:person.014560140443.76
73 rdf:rest N3972b0287f78418bae3d99203db4f71e
74 Nfe9556faed554f19bd2877f5ad1fe051 schema:name Springer Nature - SN SciGraph project
75 rdf:type schema:Organization
76 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
77 schema:name Information and Computing Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
80 schema:name Artificial Intelligence and Image Processing
81 rdf:type schema:DefinedTerm
82 sg:journal.1136120 schema:issn 1476-8186
83 1751-8520
84 schema:name International Journal of Automation and Computing
85 rdf:type schema:Periodical
86 sg:person.014560140443.76 schema:affiliation https://www.grid.ac/institutes/grid.216417.7
87 schema:familyName Zeng
88 schema:givenName Hong-Bing
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014560140443.76
90 rdf:type schema:Person
91 sg:person.07572434757.64 schema:affiliation https://www.grid.ac/institutes/grid.411431.2
92 schema:familyName Xiao
93 schema:givenName Shen-Ping
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07572434757.64
95 rdf:type schema:Person
96 sg:pub.10.1007/s11633-009-0223-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036451184
97 https://doi.org/10.1007/s11633-009-0223-3
98 rdf:type schema:CreativeWork
99 sg:pub.10.1007/s11633-009-0415-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1029197096
100 https://doi.org/10.1007/s11633-009-0415-x
101 rdf:type schema:CreativeWork
102 sg:pub.10.1007/s11633-010-0199-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1048790813
103 https://doi.org/10.1007/s11633-010-0199-z
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1002/rnc.1039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019995988
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/j.chaos.2005.04.120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020287010
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.neunet.2005.03.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003695671
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.nonrwa.2008.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052511895
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/j.physleta.2004.03.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049866761
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.physleta.2006.10.073 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039927934
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/j.physleta.2008.11.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033299909
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.physleta.2008.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052941985
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/s0893-6080(03)00192-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031729841
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1109/72.265960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218408
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1109/72.363441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218538
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1109/tcsi.2003.817760 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061564998
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/tcsi.2004.841574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061565309
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/tcsi.2006.883159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061565743
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/tcsii.2008.921597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061569950
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tnn.2006.873283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717017
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/tnn.2006.888373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717144
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/tnn.2007.903147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717281
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/tnn.2008.2001265 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717409
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/tnn.2009.2014160 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717525
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/tsmcb.2003.821455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061796274
146 rdf:type schema:CreativeWork
147 https://www.grid.ac/institutes/grid.1001.0 schema:alternateName Australian National University
148 schema:name School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC
149 School of Engineering, Australian National University, 0200, Canberra, ACT, Australia
150 rdf:type schema:Organization
151 https://www.grid.ac/institutes/grid.216417.7 schema:alternateName Central South University
152 schema:name School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC
153 School of Information Science and Engineering, Central South University, 410083, Changsha, PRC
154 rdf:type schema:Organization
155 https://www.grid.ac/institutes/grid.411431.2 schema:alternateName Hunan University of Technology
156 schema:name School of Electrical and Information Engineering, Hunan University of Technology, 412008, Zhuzhou, PRC
157 rdf:type schema:Organization
 




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


...