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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


Włodzisław Duch

Janusz Kacprzyk

Erkki Oja

Sławomir Zadrożny


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.

Start Date Sep 11, 2005
Publication Date Sep 1, 2005
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 103-108
Series Title Lecture notes in computer science
Series Number 3697
Series ISSN 0302-9743
ISBN 9783540287551
Institution Citation 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:
Keywords Taguchi methods; Networks; Nonlinear mappings; Control systems


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