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Application of adaptive differential evolution for model identification in furnace optimized control system
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-3425-3837
Industrial Systems, Prevas, Västerås, Sweden.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-9857-4317
2015 (engelsk)Inngår i: IJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015, s. 48-54Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2015. s. 48-54
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-29648Scopus ID: 2-s2.0-84961118345ISBN: 9789897581571 (tryckt)OAI: oai:DiVA.org:mdh-29648DiVA, id: diva2:876490
Konferanse
7th International Joint Conference on Computational Intelligence, IJCCI 2015; Lisbon; Portugal; 12 November 2015 through 14 November 2015
Prosjekter
EMOPAC - Evolutionary Multi-Objective Optimization and Its Applications in Analog Circuit DesignTilgjengelig fra: 2015-12-03 Laget: 2015-11-26 Sist oppdatert: 2018-02-23bibliografisk kontrollert

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