Genetic‐based on‐line learning for fuzzy process control
Identifieur interne : 004914 ( Main/Exploration ); précédent : 004913; suivant : 004915Genetic‐based on‐line learning for fuzzy process control
Auteurs : Juan R. Velasco [Espagne]Source :
- [ 0884-8173 ]
Abstract
This paper deals with the problem of continuous learning in process control. Conventional machine learning applied to process control tries to obtain control rules from an historic data file or a model. However, these learned rules may be useless if the real process changes, and this is not unusual. To try to solve this problem, genetic algorithms can be used in a continuous learning environment. However, genetically generated rules do not guarantee that they are good enough to control the process. New rules should be tested before their insertion into the knowledge base: this is the function of Limbo. Limbo is a special place where rules can be tested in real situations before being used. This paper shows how Limbo can be used to improve continuous learning. © 1998 John Wiley & Sons, Inc.
Url:
DOI: 10.1002/(SICI)1098-111X(199810/11)13:10/11<891::AID-INT2>3.0.CO;2-U
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 004B80
- to stream Istex, to step Curation: 004B80
- to stream Istex, to step Checkpoint: 003473
- to stream Main, to step Merge: 004A66
- to stream Main, to step Curation: 004914
Le document en format XML
<record><TEI wicri:istexFullTextTei="no"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genetic‐based on‐line learning for fuzzy process control</title><author><name sortKey="Velasco, Juan R" sort="Velasco, Juan R" uniqKey="Velasco J" first="Juan R." last="Velasco">Juan R. Velasco</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:B40FEC8BA451EEB6443CAE10B5C09036D284B72C</idno><date when="1998" year="1998">1998</date><idno type="doi">10.1002/(SICI)1098-111X(199810/11)13:10/11<891::AID-INT2>3.0.CO;2-U</idno><idno type="url">https://api.istex.fr/document/B40FEC8BA451EEB6443CAE10B5C09036D284B72C/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">004B80</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">004B80</idno><idno type="wicri:Area/Istex/Curation">004B80</idno><idno type="wicri:Area/Istex/Checkpoint">003473</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">003473</idno><idno type="wicri:doubleKey">0884-8173:1998:Velasco J:genetic:based:on</idno><idno type="wicri:Area/Main/Merge">004A66</idno><idno type="wicri:Area/Main/Curation">004914</idno><idno type="wicri:Area/Main/Exploration">004914</idno></publicationStmt><sourceDesc><biblStruct><analytic><author><name sortKey="Velasco, Juan R" sort="Velasco, Juan R" uniqKey="Velasco J" first="Juan R." last="Velasco">Juan R. Velasco</name><affiliation wicri:level="2"><country xml:lang="fr">Espagne</country><wicri:regionArea>Departamento Ingeniería de Sistemas Telemáticos, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid E‐28040</wicri:regionArea><placeName><region nuts="2" type="communauté">Communauté de Madrid</region></placeName></affiliation></author></analytic><series><idno type="ISSN">0884-8173</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0884-8173</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper deals with the problem of continuous learning in process control. Conventional machine learning applied to process control tries to obtain control rules from an historic data file or a model. However, these learned rules may be useless if the real process changes, and this is not unusual. To try to solve this problem, genetic algorithms can be used in a continuous learning environment. However, genetically generated rules do not guarantee that they are good enough to control the process. New rules should be tested before their insertion into the knowledge base: this is the function of Limbo. Limbo is a special place where rules can be tested in real situations before being used. This paper shows how Limbo can be used to improve continuous learning. © 1998 John Wiley & Sons, Inc.</div></front></TEI><affiliations><list><country><li>Espagne</li></country><region><li>Communauté de Madrid</li></region></list><tree><country name="Espagne"><region name="Communauté de Madrid"><name sortKey="Velasco, Juan R" sort="Velasco, Juan R" uniqKey="Velasco J" first="Juan R." last="Velasco">Juan R. Velasco</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/TelematiV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 004914 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 004914 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= TelematiV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:B40FEC8BA451EEB6443CAE10B5C09036D284B72C
|texte= Genetic‐based on‐line learning for fuzzy process control
}}
| This area was generated with Dilib version V0.6.31. |  |