Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Xing, Guishan and Ding, Jinliang and Chai, Tianyou and Afshar, Puya and Wang, Hong (2012) Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process. Engineering Applications of Arti�cial Intelligence, 25 (2). pp. 418-429.

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Abstract

In this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in of�ine mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation ofdifferenttypesofcoolingprocess.

Item Type:	Article
Additional Information:	cicada
Uncontrolled Keywords:	cicada
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 90 Operations research, mathematical programming MSC 2010, the AMS's Mathematics Subject Classification > 93 Systems theory; control
Depositing User:	Mr Houman Dallali
Date Deposited:	16 Jan 2012
Last Modified:	20 Oct 2017 14:13
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/1760

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