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Industrial process error estimation by machine learning
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Feluppskattning i industriella processer genom maskininlärning (Swedish)
Abstract [en]

Performing a set-up on a complex machine may be difficult. This problem arises frequently for the industry, especially when the relation between input and output data cannot be defined precisely. Heavy methods of optimization may be used to perform a set-up. This master thesis investigate the possibility to use a machine learning approach on a specific machine. We study the structure of the relation between input and output data. We show the variations are smooth. We define a set of tests to evaluate future models. We design and test several models on simulation data, and select the best one. We design a strategy to use data in the best possible way. The selected model is then tested on actual data in order to be optimized.

Abstract [sv]

Att justera ett komplext maskineri kan vara mödosamt. Detta problem uppkommer ofta i industrin när relationen mellan in- och utdata inte kan definieras.

Tunga optimeringsmetoder kan användas för justeringprocessen. Dennauppsats undersöker möjligheten att använda en s.k. machine learning approach med ett specifikt maskineri. Vi studerar datastrukturen och relationen mellan in- och utdata. Vi visar på att variationer är släta. Vi definierar en rad av tester för att värdera framtida modeller. Vi formger och testar flera modeller med simulerade data och välja den bästa. Vi designar en strategi för att använda data på bästa sätt, för att sedan testa den utvalda modellen på verklig data för att optimeras.

Place, publisher, year, edition, pages
2015.
Series
TRITA-MAT-E, 2015:85
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-178069OAI: oai:DiVA.org:kth-178069DiVA: diva2:877139
External cooperation
Saint-Gobain Recherche S.A., Aubervilliers
Subject / course
Scientific Computing
Educational program
Master of Science in Engineering -Engineering Physics
Supervisors
Examiners
Available from: 2015-12-05 Created: 2015-12-05 Last updated: 2015-12-05Bibliographically approved

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Numerical Analysis, NA
Computational Mathematics

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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  • Other locale
More languages
Output format
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