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Application of adaptive differential evolution for model identification in furnace optimized control system
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1965-0532
Industrial Systems, Prevas, Västerås, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2015 (English)In: IJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015, 48-54 p.Conference paper, Published paper (Refereed)
Abstract [en]

Accurate system modelling is an important prerequisite for optimized process control in modern industrial scenarios. The task of parameter identification for a model can be considered as an optimization problem of searching for a set of continuous parameters to minimize the discrepancy between the model outputs and true output values. Differential Evolution (DE), as a class of population-based and global search algorithms, has strong potential to be employed here to solve this problem. Nevertheless, the performance of DE is rather sensitive to its two running parameters: scaling factor and crossover rate. Improper setting of these two parameters may cause weak performance of DE in real applications. This paper presents a new adaptive algorithm for DE, which does not require good parameter values to be specified by users in advance. Our new algorithm is established by integration of greedy search into the original DE algorithm. Greedy search is conducted repeatedly during the running of DE to reach better parameter assignments in the neighborhood. We have applied our adaptive DE algorithm for process model identification in a Furnace Optimized Control System (FOCS). The experiment results revealed that our adaptive DE algorithm yielded process models that estimated temperatures inside a furnace more precisely than those produced by using the original DE algorithm.

Place, publisher, year, edition, pages
2015. 48-54 p.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-29648Scopus ID: 2-s2.0-84961118345ISBN: 9789897581571 (print)OAI: oai:DiVA.org:mdh-29648DiVA: diva2:876490
Conference
7th International Joint Conference on Computational Intelligence, IJCCI 2015; Lisbon; Portugal; 12 November 2015 through 14 November 2015
Projects
EMOPAC - Evolutionary Multi-Objective Optimization and Its Applications in Analog Circuit Design
Available from: 2015-12-03 Created: 2015-11-26 Last updated: 2016-12-27Bibliographically approved

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CiteExportLink to record
Permanent link

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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