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

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Abstract

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.

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Engineering, Chiang Mai University, Thailand

    Wimalin Sukthomya

  2. Nottingham University Business School, University of Nottingham, UK

    James Tannock

Authors
  1. Wimalin Sukthomya
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  2. James Tannock
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Correspondence to James Tannock.

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Sukthomya, W., Tannock, J. The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling. Neural Comput & Applic 14, 337–344 (2005). https://doi.org/10.1007/s00521-005-0470-3

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  • DOI: https://doi.org/10.1007/s00521-005-0470-3

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