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The investigation of model selection criteria in artificial neural networks by the Taguchi method

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  • Tortum, Ahmet
  • Yayla, Nadir
  • Çelik, Cafer
  • Gökdağ, Mahir

Abstract

Artificial neural networks (ANNs) have been successfully used for solving variety of problems. One major disadvantage of ANNs is that there is no formal systematic model building approach. This paper presents the application of the Taguchi method in the optimization of the design parameters of the ANNs. The performances of the ANNs were determined by the Taguchi method considering factors relevant for ANNs’ performance. The properties affecting the performance of the ANNs and their levels on the peak analytical function were determined by performing computational experiments. After training the network, the values of the statistical data criteria were determined and the optimum parameter levels were obtained in terms of the performance statistics. The performance of ANNs is shown to be better in the case of the application of the Taguchi method rather than in the case of random choice of factor values.

Suggested Citation

  • Tortum, Ahmet & Yayla, Nadir & Çelik, Cafer & Gökdağ, Mahir, 2007. "The investigation of model selection criteria in artificial neural networks by the Taguchi method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 446-468.
    • Handle: RePEc:eee:phsmap:v:386:y:2007:i:1:p:446-468
    DOI: 10.1016/j.physa.2007.07.064
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    References listed on IDEAS

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    1. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    2. Franses, Philip Hans & Draisma, Gerrit, 1997. "Recognizing changing seasonal patterns using artificial neural networks," Journal of Econometrics, Elsevier, vol. 81(1), pages 273-280, November.
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    1. Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.

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