An incremental learning algorithm for functional approximation
Identifieur interne : 003784 ( Main/Exploration ); précédent : 003783; suivant : 003785An incremental learning algorithm for functional approximation
Auteurs : Jacques Bahi [France] ; Sylvain Contassot-Vivier [France] ; Marc Sauget [France]Source :
- Advances in Engineering Software [ 0965-9978 ] ; 2009.
English descriptors
Abstract
This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts of synthetic data and of real data consisting of radiation doses in homogeneous environments.
Url:
DOI: 10.1016/j.advengsoft.2008.12.018
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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<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>
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<desc><address><country key="FR"></country>
</address>
</desc>
</org>
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<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>
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</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>
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</author>
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<idno type="DOI">10.1016/j.advengsoft.2008.12.018</idno>
<series><title level="j">Advances in Engineering Software</title>
<idno type="ISSN">0965-9978</idno>
<imprint><date type="datePub">2009</date>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Incremental Learning</term>
<term>Neural networks</term>
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<front><div type="abstract" xml:lang="en">This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts of synthetic data and of real data consisting of radiation doses in homogeneous environments.</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>
</country>
</tree>
</affiliations>
</record>
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