A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
Identifieur interne : 003F43 ( Main/Exploration ); précédent : 003F42; suivant : 003F44A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
Auteurs : Jacques Bahi [France] ; Sylvain Contassot-Vivier [France] ; Marc Sauget [France] ; Aurélien Vasseur [France]Source :
English descriptors
Abstract
This paper presents a parallel and fault tolerant version of an incremental learning algorithm for feed-forward neural networks used as function approximators. It has been shown in previous works that our incremental algorithm builds networks of reduced size while providing high quality approximations for real data sets. However, for very large sets, the use of our learning process on a single machine may be quite long and even sometimes impossible, due to memory limitations. The parallel algorithm presented in this paper is usable in any parallel system, and in particular, with large dynamical systems such as clusters and grids in which faults may occur. Finally, the quality and performances (without and with faults) of that algorithm are experimentally evaluated.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Belfort, Besançon, Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine, Université de technologie de Belfort-Montbéliard
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Le document en format XML
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key="FR"></country></address><ref type="url">http://www.univ-fcomte.fr</ref></desc></org></tutelle><tutelle active="#struct-300261" type="direct"><org type="institution" xml:id="struct-300261" status="VALID"><orgName>Université de Technologie de Belfort-Montbeliard</orgName><orgName type="acronym">UTBM</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300360" type="direct"><org type="institution" xml:id="struct-300360" status="VALID"><orgName>Ecole Nationale Supérieure de Mécanique et des Microtechniques</orgName><orgName type="acronym">ENSMM</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle name="UMR6174" active="#struct-441569" type="direct"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city" wicri:auto="siege">Besançon</settlement><region type="region" nuts="2">Franche-Comté</region></placeName><orgName type="university">Université de Franche-Comté</orgName><orgName type="institution" wicri:auto="newGroup">Université de Bourgogne Franche-Comté</orgName><placeName><settlement type="city" wicri:auto="siege">Belfort</settlement><region type="region" nuts="2">Franche-Comté</region></placeName><orgName type="university">Université de technologie de Belfort-Montbéliard</orgName></affiliation></author><author><name sortKey="Vasseur, Aurelien" sort="Vasseur, Aurelien" uniqKey="Vasseur A" first="Aurélien" last="Vasseur">Aurélien Vasseur</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-866" status="VALID"><idno type="IdRef">152639071</idno><idno type="RNSR">200412232H</idno><orgName>Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies</orgName><orgName type="acronym">FEMTO-ST</orgName><desc><address><addrLine>32 avenue de l'Observatoire 25044 BESANCON CEDEX</addrLine><country key="FR"></country></address><ref type="url">http://www.femto-st.fr</ref></desc><listRelation><relation active="#struct-242365" type="direct"></relation><relation active="#struct-300261" type="direct"></relation><relation active="#struct-300360" type="direct"></relation><relation name="UMR6174" active="#struct-441569" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-242365" type="direct"><org type="institution" xml:id="struct-242365" status="VALID"><idno type="IdRef">026403188</idno><idno type="ISNI">0000 0001 2188 3779 </idno><orgName>Université de Franche-Comté</orgName><orgName type="acronym">UFC</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.univ-fcomte.fr</ref></desc></org></tutelle><tutelle active="#struct-300261" type="direct"><org type="institution" xml:id="struct-300261" status="VALID"><orgName>Université de Technologie de Belfort-Montbeliard</orgName><orgName type="acronym">UTBM</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300360" type="direct"><org type="institution" xml:id="struct-300360" status="VALID"><orgName>Ecole Nationale Supérieure de Mécanique et des Microtechniques</orgName><orgName type="acronym">ENSMM</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle name="UMR6174" active="#struct-441569" type="direct"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city" wicri:auto="siege">Besançon</settlement><region type="region" nuts="2">Franche-Comté</region></placeName><orgName type="university">Université de Franche-Comté</orgName><orgName type="institution" wicri:auto="newGroup">Université de Bourgogne Franche-Comté</orgName><placeName><settlement type="city" wicri:auto="siege">Belfort</settlement><region type="region" nuts="2">Franche-Comté</region></placeName><orgName type="university">Université de technologie de Belfort-Montbéliard</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Learning algorithms</term><term>Neural Networks</term><term>Parallelism</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents a parallel and fault tolerant version of an incremental learning algorithm for feed-forward neural networks used as function approximators. It has been shown in previous works that our incremental algorithm builds networks of reduced size while providing high quality approximations for real data sets. However, for very large sets, the use of our learning process on a single machine may be quite long and even sometimes impossible, due to memory limitations. The parallel algorithm presented in this paper is usable in any parallel system, and in particular, with large dynamical systems such as clusters and grids in which faults may occur. Finally, the quality and performances (without and with faults) of that algorithm are experimentally evaluated.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Belfort</li><li>Besançon</li><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li><li>Université de technologie de Belfort-Montbéliard</li></orgName></list><tree><country name="France"><region name="Franche-Comté"><name sortKey="Bahi, Jacques" sort="Bahi, Jacques" uniqKey="Bahi J" first="Jacques" last="Bahi">Jacques Bahi</name></region><name sortKey="Contassot Vivier, Sylvain" sort="Contassot Vivier, Sylvain" uniqKey="Contassot Vivier S" first="Sylvain" last="Contassot-Vivier">Sylvain Contassot-Vivier</name><name sortKey="Sauget, Marc" sort="Sauget, Marc" uniqKey="Sauget M" first="Marc" last="Sauget">Marc Sauget</name><name sortKey="Vasseur, Aurelien" sort="Vasseur, Aurelien" uniqKey="Vasseur A" first="Aurélien" last="Vasseur">Aurélien Vasseur</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
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