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Optimal input selection for neural fuzzy modelling with application to Charpy energy prediction

Yang, Yong Y. and Mahfouf, Mahdi and Zhang, Qian (2011) Optimal input selection for neural fuzzy modelling with application to Charpy energy prediction. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). IEEE, pp. 2756-2762. ISBN 978-1-4244-7315-1. E-ISBN 978-1-4244-7316-8. (doi:10.1109/FUZZY.2011.6007735) (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)

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. (Contact us about this Publication)
Official URL
http://doi.org/10.1109/FUZZY.2011.6007735

Abstract

Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based forward input selection scheme is proposed. The objective of the proposed scheme is to select the smallest number of important variables as model inputs, which will then be used for neural-fuzzy data modelling. The proposed input selection scheme is applied to a case study of Charpy impact energy prediction, with data extracted from an industrial database. Model performance has been compared with previous results where a much larger input set was used. Simulation results show that the number of inputs for the Charpy data model can be significantly reduced with little performance degradation. Also, the performance of the proposed scheme outperforms both the standard correlation analysis and fuzzy clustering based input selection schemes.

Item Type: Book section
DOI/Identification number: 10.1109/FUZZY.2011.6007735
Uncontrolled keywords: artificial neural networks; data models; input variables; correlation; training; computational modeling; neurons
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: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:37 UTC
Last Modified: 20 Sep 2019 10:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50543 (The current URI for this page, for reference purposes)
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