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Training neural networks using Taguchi methods: overcoming interaction problems.

Viswanathan, Alagappan; MacLeod, Christopher; Maxwell, Grant; Kalidindi, Sashank


Alagappan Viswanathan

Christopher MacLeod

Grant Maxwell

Sashank Kalidindi


W?odzis?aw Duch

Janusz Kacprzyk

Erkki Oja

S?awomir Zadro?ny


Taguchi Methods (and other orthogonal arrays) may be used to train small Artificial Neural Networks very quickly in a variety of tasks. These include, importantly, Control Systems. Previous experimental work has shown that they could be successfully used to train single layer networks with no difficulty. However, interaction between layers precluded the successful reliable training of multi-layered networks. This paper describes a number of successful strategies which may be used to overcome this problem and demonstrates the ability of such networks to learn non-linear mappings.


VISWANATHAN, A., MACLEOD, C., MAXWELL, G. and KALIDINDI, S. 2005. Training neural networks using Taguchi methods: overcoming interaction problems. In Duch, W., Kacprzyk, J., Oja, E. and Zadrozy, S (eds.) Artificial neural networks: formal models and their applications, part 2; proceedings of the 15th International on artificial neural networks (ICAAN 2005), 11-15 September 2005, Warsaw, Poland. Lecture notes in computer science, 3697. Berlin: Springer, pages 103-108. Available from:

Conference Name 15th International conference on artificial neural networks (ICAAN 2005): formal models and their applications
Conference Location Warsaw, Poland
Start Date Sep 11, 2005
End Date Sep 15, 2005
Acceptance Date Sep 11, 2005
Online Publication Date Dec 31, 2005
Publication Date Dec 31, 2005
Deposit Date Sep 16, 2016
Publicly Available Date Sep 16, 2016
Print ISSN 0302-9743
Publisher Springer
Pages 103-108
Series Title Lecture notes in computer science
Series Number 3697
Series ISSN 0302-9743
ISBN 9783540287551
Keywords Taguchi methods; Networks; Nonlinear mappings; Control systems
Public URL


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