The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling

The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling

Neural networks have been widely used in manufacturing industry, but they suffer from a lack of structured method to determine the settings of NN design and training parameters, which are usually set by trial and error. This article presents an application of Taguchi's Design of Experiments, to...

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Main Authors: Wimalin Sukthomya, James Tannock
Format: Journal
Published: 2018
Subjects:
Computer Science
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=27744515720&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/62160
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-621602018-09-11T09:22:54Z The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling Wimalin Sukthomya James Tannock Computer Science Neural networks have been widely used in manufacturing industry, but they suffer from a lack of structured method to determine the settings of NN design and training parameters, which are usually set by trial and error. This article presents an application of Taguchi's Design of Experiments, to identify the optimum setting of NN parameters in a multilayer perceptron (MLP) network trained with the back propagation algorithm. A case study of a complex forming process is used to demonstrate implementation of the approach in manufacturing, and the issues arising from the case are discussed. © Springer-Verlag London Limited 2005. 2018-09-11T09:22:54Z 2018-09-11T09:22:54Z 2005-12-01 Journal 09410643 2-s2.0-27744515720 10.1007/s00521-005-0470-3 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=27744515720&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/62160
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Wimalin Sukthomya
James Tannock
The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
description Neural networks have been widely used in manufacturing industry, but they suffer from a lack of structured method to determine the settings of NN design and training parameters, which are usually set by trial and error. This article presents an application of Taguchi's Design of Experiments, to identify the optimum setting of NN parameters in a multilayer perceptron (MLP) network trained with the back propagation algorithm. A case study of a complex forming process is used to demonstrate implementation of the approach in manufacturing, and the issues arising from the case are discussed. © Springer-Verlag London Limited 2005.
format Journal
author Wimalin Sukthomya
James Tannock
author_facet Wimalin Sukthomya
James Tannock
author_sort Wimalin Sukthomya
title The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
title_short The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
title_full The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
title_fullStr The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
title_full_unstemmed The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
title_sort optimisation of neural network parameters using taguchi's design of experiments approach: an application in manufacturing process modelling
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=27744515720&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/62160
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