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 N93410ca0ca2e48aab1a83eaa91dd1302
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 N8192a6ecb2d549ce9a66a0aa397c8a33
35 Nbd718fc521cd445192a9a849f61e7a5b
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 Nd13382e70e3d4980820db55c9304a95c
40 Nd675a06868134e70a5d8e048174303ea
41 Nddb253badfa04209a2a70ae1ac5e0883
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 N149a0e55bdc548dda5a92474521b817f
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 N149a0e55bdc548dda5a92474521b817f schema:name Springer Nature - SN SciGraph project
52 rdf:type schema:Organization
53 N51c757f47d984a32a6b9b993ab855946 rdf:first sg:person.07572434757.64
54 rdf:rest Nad2ff0102f1042f898192bdfa37e0742
55 N8192a6ecb2d549ce9a66a0aa397c8a33 schema:volumeNumber 8
56 rdf:type schema:PublicationVolume
57 N93410ca0ca2e48aab1a83eaa91dd1302 rdf:first sg:person.014560140443.76
58 rdf:rest N51c757f47d984a32a6b9b993ab855946
59 Na2590a65d7f94d5590298d0e54686631 schema:affiliation https://www.grid.ac/institutes/grid.1001.0
60 schema:familyName Liu
61 schema:givenName Bin
62 rdf:type schema:Person
63 Nad2ff0102f1042f898192bdfa37e0742 rdf:first Na2590a65d7f94d5590298d0e54686631
64 rdf:rest rdf:nil
65 Nbd718fc521cd445192a9a849f61e7a5b schema:issueNumber 1
66 rdf:type schema:PublicationIssue
67 Nd13382e70e3d4980820db55c9304a95c schema:name readcube_id
68 schema:value 81d0e945acf3c8f338218ae37df04006c873cad1dff35d035e59163ab779d0cd
69 rdf:type schema:PropertyValue
70 Nd675a06868134e70a5d8e048174303ea schema:name dimensions_id
71 schema:value pub.1027251551
72 rdf:type schema:PropertyValue
73 Nddb253badfa04209a2a70ae1ac5e0883 schema:name doi
74 schema:value 10.1007/s11633-010-0564-y
75 rdf:type schema:PropertyValue
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)


...