Authors:
Miguel Leon
1
;
Magnus Evestedt
2
and
Ning Xiong
1
Affiliations:
1
Malardalen University, Sweden
;
2
Prevas, Sweden
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
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.
(More)