Mahfouf, Mahdi, Yang, Yong Y., Zhang, Qian (2009) Modelling Charpy impact energy of heat-treated-steel using efficient neural-networks & genetic-algorithms. In: Automation in Mining, Mineral and Metal Processing. 1 (1). pp. 237-242. (doi:10.3182/20091014-3-CL-4011.00043) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50548)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://doi.org/10.3182/20091014-3-CL-4011.00043 |
Abstract
Although neural networks (NN) are known to represent a powerful tool for mapping non-linear relationships between the inputs and outputs, their structures are typically set up by the modeller either by trial-and-error or based on their own experience. The latter is not only a time-consuming operation but also creates a risk that the best model structure is not necessarily selected. In this paper a genetic algorithm (GA) based strategy of determining the optimal NN-based model structure, together with the best training options for NN modelling will be described. Initial results of this GA-NN based modelling paradigm are promising, with the model performance being significantly improved when compared to previously elicited where the model structures were defined in an 'ad-hoc' fashion.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.3182/20091014-3-CL-4011.00043 |
Subjects: |
Q Science > Q Science (General) > Q335 Artificial intelligence T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Qian Zhang |
Date Deposited: | 18 Sep 2015 16:03 UTC |
Last Modified: | 16 Nov 2021 10:21 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/50548 (The current URI for this page, for reference purposes) |
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