InforLorV4, PascalFrancis, Corpus, bibRecord, 000883

Knowledge extraction using artificial neural networks: application to radar target identification

Identifieur interne : 000883 ( PascalFrancis/Corpus ); précédent : 000882; suivant : 000884

Knowledge extraction using artificial neural networks: application to radar target identification

Auteurs : J.-F. Remm ; F. Alexandre

Source :

Signal processing [ 0165-1684 ] ; 2002.

RBID : Pascal:02-0200588

Descripteurs français

Pascal (Inist)
- Réseau neuronal, Extraction caractéristique, Détection cible, Radar, Perceptron multicouche, Traitement signal.

English descriptors

KwdEn :
- Feature extraction, Multilayer perceptrons, Neural network, Radar, Signal processing, Target detection.

Abstract

Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0165-1684`
A02	`01`			`@0 SPRODR`
A03		`1`		`@0 Signal process.`
A05				`@2 82`
A06				`@2 1`
A08	`01`	`1`	`ENG`	`@1 Knowledge extraction using artificial neural networks: application to radar target identification`
A11	`01`	`1`		`@1 REMM (J.-F.)`
A11	`02`	`1`		`@1 ALEXANDRE (F.)`
A14	`01`			`@1 LORIA-INRIA, BP 239 @2 54506 Vandœuvre @3 FRA @Z 1 aut. @Z 2 aut.`
A20				`@1 117-120`
A21				`@1 2002`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 18015 @5 354000102253900080`
A44				`@0 0000 @1 © 2002 INIST-CNRS. All rights reserved.`
A45				`@0 8 ref.`
A47	`01`	`1`		`@0 02-0200588`
A60				`@1 P @3 CC`
A61				`@0 A`
A64	`01`	`1`		`@0 Signal processing`
A66	`01`			`@0 NLD`
C01	`01`		`ENG`	`@0 Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.`
C02	`01`	`X`		`@0 001D02C06`
C02	`02`	`X`		`@0 001D04B05`
C02	`03`	`X`		`@0 001D04A05C`
C03	`01`	`X`	`FRE`	`@0 Réseau neuronal @5 01`
C03	`01`	`X`	`ENG`	`@0 Neural network @5 01`
C03	`01`	`X`	`SPA`	`@0 Red neuronal @5 01`
C03	`02`	`1`	`FRE`	`@0 Extraction caractéristique @5 02`
C03	`02`	`1`	`ENG`	`@0 Feature extraction @5 02`
C03	`03`	`X`	`FRE`	`@0 Détection cible @5 03`
C03	`03`	`X`	`ENG`	`@0 Target detection @5 03`
C03	`03`	`X`	`SPA`	`@0 Detección blanco @5 03`
C03	`04`	`X`	`FRE`	`@0 Radar @5 04`
C03	`04`	`X`	`ENG`	`@0 Radar @5 04`
C03	`04`	`X`	`SPA`	`@0 Radar @5 04`
C03	`05`	`3`	`FRE`	`@0 Perceptron multicouche @5 05`
C03	`05`	`3`	`ENG`	`@0 Multilayer perceptrons @5 05`
C03	`06`	`X`	`FRE`	`@0 Traitement signal @5 06`
C03	`06`	`X`	`ENG`	`@0 Signal processing @5 06`
C03	`06`	`X`	`SPA`	`@0 Procesamiento señal @5 06`
N21				`@1 119`
N82				`@1 PSI`

Format Inist (serveur)

NO :	PASCAL 02-0200588 INIST
ET :	Knowledge extraction using artificial neural networks: application to radar target identification
AU :	REMM (J.-F.); ALEXANDRE (F.)
AF :	LORIA-INRIA, BP 239/54506 Vandœuvre/France (1 aut., 2 aut.)
DT :	Publication en série; Courte communication, note brève; Niveau analytique
SO :	Signal processing; ISSN 0165-1684; Coden SPRODR; Pays-Bas; Da. 2002; Vol. 82; No. 1; Pp. 117-120; Bibl. 8 ref.
LA :	Anglais
EA :	Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.
CC :	001D02C06; 001D04B05; 001D04A05C
FD :	Réseau neuronal; Extraction caractéristique; Détection cible; Radar; Perceptron multicouche; Traitement signal
ED :	Neural network; Feature extraction; Target detection; Radar; Multilayer perceptrons; Signal processing
SD :	Red neuronal; Detección blanco; Radar; Procesamiento señal
LO :	INIST-18015.354000102253900080
ID :	02-0200588

Links to Exploration step

Pascal:02-0200588

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Knowledge extraction using artificial neural networks: application to radar target identification</title>
<author><name sortKey="Remm, J F" sort="Remm, J F" uniqKey="Remm J" first="J.-F." last="Remm">J.-F. Remm</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-INRIA, BP 239</s1>
<s2>54506 Vandœuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-INRIA, BP 239</s1>
<s2>54506 Vandœuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">02-0200588</idno>
<date when="2002">2002</date>
<idno type="stanalyst">PASCAL 02-0200588 INIST</idno>
<idno type="RBID">Pascal:02-0200588</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000883</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Knowledge extraction using artificial neural networks: application to radar target identification</title>
<author><name sortKey="Remm, J F" sort="Remm, J F" uniqKey="Remm J" first="J.-F." last="Remm">J.-F. Remm</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-INRIA, BP 239</s1>
<s2>54506 Vandœuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-INRIA, BP 239</s1>
<s2>54506 Vandœuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Signal processing</title>
<title level="j" type="abbreviated">Signal process.</title>
<idno type="ISSN">0165-1684</idno>
<imprint><date when="2002">2002</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Signal processing</title>
<title level="j" type="abbreviated">Signal process.</title>
<idno type="ISSN">0165-1684</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Feature extraction</term>
<term>Multilayer perceptrons</term>
<term>Neural network</term>
<term>Radar</term>
<term>Signal processing</term>
<term>Target detection</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Réseau neuronal</term>
<term>Extraction caractéristique</term>
<term>Détection cible</term>
<term>Radar</term>
<term>Perceptron multicouche</term>
<term>Traitement signal</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0165-1684</s0>
</fA01>
<fA02 i1="01"><s0>SPRODR</s0>
</fA02>
<fA03 i2="1"><s0>Signal process.</s0>
</fA03>
<fA05><s2>82</s2>
</fA05>
<fA06><s2>1</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>Knowledge extraction using artificial neural networks: application to radar target identification</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>REMM (J.-F.)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>ALEXANDRE (F.)</s1>
</fA11>
<fA14 i1="01"><s1>LORIA-INRIA, BP 239</s1>
<s2>54506 Vandœuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA20><s1>117-120</s1>
</fA20>
<fA21><s1>2002</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>18015</s2>
<s5>354000102253900080</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2002 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>8 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>02-0200588</s0>
</fA47>
<fA60><s1>P</s1>
<s3>CC</s3>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Signal processing</s0>
</fA64>
<fA66 i1="01"><s0>NLD</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>001D02C06</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>001D04B05</s0>
</fC02>
<fC02 i1="03" i2="X"><s0>001D04A05C</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Réseau neuronal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Neural network</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Red neuronal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="1" l="FRE"><s0>Extraction caractéristique</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="1" l="ENG"><s0>Feature extraction</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Détection cible</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Target detection</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Detección blanco</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Radar</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Radar</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Radar</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="3" l="FRE"><s0>Perceptron multicouche</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="3" l="ENG"><s0>Multilayer perceptrons</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Traitement signal</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Signal processing</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Procesamiento señal</s0>
<s5>06</s5>
</fC03>
<fN21><s1>119</s1>
</fN21>
<fN82><s1>PSI</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 02-0200588 INIST</NO>
<ET>Knowledge extraction using artificial neural networks: application to radar target identification</ET>
<AU>REMM (J.-F.); ALEXANDRE (F.)</AU>
<AF>LORIA-INRIA, BP 239/54506 Vandœuvre/France (1 aut., 2 aut.)</AF>
<DT>Publication en série; Courte communication, note brève; Niveau analytique</DT>
<SO>Signal processing; ISSN 0165-1684; Coden SPRODR; Pays-Bas; Da. 2002; Vol. 82; No. 1; Pp. 117-120; Bibl. 8 ref.</SO>
<LA>Anglais</LA>
<EA>Artificial neural networks are efficient for performing signal processing but are not able to explain their decision nor to extract knowledge from data. We propose here a way to extract rules and hints from the hidden layers of a multilayered perceptron. The network is first pruned and then the progressive use of a simpler transfer function can allow such knowledge extraction. This method has been successfully applied to radar signal identification.</EA>
<CC>001D02C06; 001D04B05; 001D04A05C</CC>
<FD>Réseau neuronal; Extraction caractéristique; Détection cible; Radar; Perceptron multicouche; Traitement signal</FD>
<ED>Neural network; Feature extraction; Target detection; Radar; Multilayer perceptrons; Signal processing</ED>
<SD>Red neuronal; Detección blanco; Radar; Procesamiento señal</SD>
<LO>INIST-18015.354000102253900080</LO>
<ID>02-0200588</ID>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/PascalFrancis/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000883 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000883 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:02-0200588
   |texte=   Knowledge extraction using artificial neural networks: application to radar target identification
}}

This area was generated with Dilib version V0.6.33.
Data generation: Mon Jun 10 21:56:28 2019. Site generation: Fri Feb 25 15:29:27 2022

	Serveur d'exploration sur la recherche en informatique en Lorraine
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur la recherche en informatique en Lorraine

Knowledge extraction using artificial neural networks: application to radar target identification

Knowledge extraction using artificial neural networks: application to radar target identification

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri