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Authors: Miguel Leon 1 ; Magnus Evestedt 2 and Ning Xiong 1

Affiliations: 1 Malardalen University, Sweden ; 2 Prevas, Sweden

ISBN: 978-989-758-157-1

Keyword(s): Differential Evolution, Optimization, Model Identification, Temperature Estimation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Hybrid Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: 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 o f 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. (More)

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Paper citation in several formats:
Leon, M.; Evestedt, M. and Xiong, N. (2015). Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 48-54. DOI: 10.5220/0005591300480054

@conference{ecta15,
author={Miguel Leon. and Magnus Evestedt. and Ning Xiong.},
title={Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={48-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005591300480054},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Application of Adaptive Differential Evolution for Model Identification in Furnace Optimized Control System
SN - 978-989-758-157-1
AU - Leon, M.
AU - Evestedt, M.
AU - Xiong, N.
PY - 2015
SP - 48
EP - 54
DO - 10.5220/0005591300480054

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