oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evolutionary computation based system decomposition with neural networks
Ilmenau University of Technology, Germany. (Neuroinformatics and Cognitive Robotics Lab)
Örebro University, School of Science and Technology. (Mobile Robotics and Olfaction Lab)
Ilmenau University of Technology, Germany. (Neuroinformatics and Cognitive Robotics Lab)
Ilmenau University of Technology, Germany. (Neuroinformatics and Cognitive Robotics Lab)
2013 (English)In: ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligenceand Machine Learning, Louvain-La-Neuve, 2013, 191-196 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present an evolutionary approach to divide a complex control system into smaller sub-systems with the help of neural networks.Thereto, measured channels are partitioned into several disjunct sets, rep-resenting possible sub-problems, while the networks are used to assessthe quality of the resulting decomposition. We show that this approach iswell suited to calculate correct decompositions of complex control systems.Furthermore, the obtained neural networks are used to predict importantprocess factors with considerable better approximation quality than mono-lithic approaches that have to deal with all input channels in parallel.

Place, publisher, year, edition, pages
Louvain-La-Neuve, 2013. 191-196 p.
Keyword [en]
neural nets, evolutionary algorithm, system decomposition
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science; Automatic Control
Identifiers
URN: urn:nbn:se:oru:diva-30519ISBN: 9782874190810 (print)OAI: oai:DiVA.org:oru-30519DiVA: diva2:644383
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2013
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2017-10-17Bibliographically approved

Open Access in DiVA

No full text

Other links

http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-58.pdf

Authority records BETA

Schaffernicht, Erik

Search in DiVA

By author/editor
Schaffernicht, Erik
By organisation
School of Science and Technology
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 871 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf