Modelling Charpy impact energy of heat-treated-steel using efficient neural-networks & genetic-algorithms

Mahfouf, Mahdi and Yang, Yong Y. and 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. pp. 237-242. (doi:https://doi.org/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)

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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)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:03 UTC
Last Modified: 22 Sep 2015 08:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50548 (The current URI for this page, for reference purposes)
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