Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments

M. S. Packianather, P. R. Drake, H. Rowlands

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

The size and training parameters of artificial neural networks have a critical effect on their performance. This paper presents the application of the Taguchi Design of Experiments (DoEs) off-line quality control method in the optimization of the design parameters of a neural network. Being a 'parallel' approach, the method offers considerable benefits in time and accuracy when compared with the conventional serial approach of trial and error. The use of the Taguchi method ensures that the quality of the neural network is taken into account at the design stage. The interpretation of the experimental results is based on the statistical technique known as analysis of variance (ANOVA). The signal-to-noise ratio (S/N) is used in designing a robust neural network that is less sensitive to noise. The effect of design parameters and neural network behaviour are also revealed as a result. Although a Wood Veneer Inspection Neural Network (WVINN) is the particular application presented here, the design methodology can be applied to neural networks in general.

Original languageEnglish
Pages (from-to)461-473
Number of pages13
JournalQuality and Reliability Engineering International
Volume16
Issue number6
DOIs
Publication statusPublished - Nov 2000
Externally publishedYes

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