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: Sukthomya W., Tannock J.
Format: Article
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-27744515720&partnerID=40&md5=a0d124871a09126d1b539de3eb6fe205
http://cmuir.cmu.ac.th/handle/6653943832/1270
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-12702014-08-29T09:29:02Z The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling Sukthomya W. Tannock J. 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. 2014-08-29T09:29:02Z 2014-08-29T09:29:02Z 2005 Article 09410643 10.1007/s00521-005-0470-3 http://www.scopus.com/inward/record.url?eid=2-s2.0-27744515720&partnerID=40&md5=a0d124871a09126d1b539de3eb6fe205 http://cmuir.cmu.ac.th/handle/6653943832/1270 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
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 Article
author Sukthomya W.
Tannock J.
spellingShingle Sukthomya W.
Tannock J.
The optimisation of neural network parameters using Taguchi's design of experiments approach: An application in manufacturing process modelling
author_facet Sukthomya W.
Tannock J.
author_sort Sukthomya W.
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 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-27744515720&partnerID=40&md5=a0d124871a09126d1b539de3eb6fe205
http://cmuir.cmu.ac.th/handle/6653943832/1270
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