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Neural networks: a need for caution

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  • Curry, B.
  • Morgan, P.

Abstract

This paper deals with the computational aspects of neural networks. Specifically, it is suggested that the now traditional method of backpropagation (BP) may not be the most appropriate basis for learning. The argument is based on the known deficiencies of gradient descent methods, of which BP is an application. Simulation results also suggest that improved performance may be obtained by employing direct optimization procedures such as the polytope algorithm. The main reason for such performance differences appears to be that the root mean square function is subject to narrow 'valleys' and other anomalies.

Suggested Citation

  • Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    • Handle: RePEc:eee:jomega:v:25:y:1997:i:1:p:123-133
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    References listed on IDEAS

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    1. Hung, Ming S. & Denton, James W., 1993. "Training neural networks with the GRG2 nonlinear optimizer," European Journal of Operational Research, Elsevier, vol. 69(1), pages 83-91, August.
    2. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
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    4. Nicholas Wilson & Kwee Chong & Michael Peel & A. N. Kolmogorov, 1995. "Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 2(1), pages 31-50.
    5. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
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    Cited by:

    1. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    2. Curry, B. & Morgan, P. H., 2004. "Evaluating Kohonen's learning rule: An approach through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 154(1), pages 191-205, April.
    3. G Johnes, 2003. "Curriculum," Working Papers 541985, Lancaster University Management School, Economics Department.
    4. West, David A. & Mangiameli, Paul M. & Chen, Shaw K., 1999. "Control of complex manufacturing processes: a comparison of SPC methods with a radial basis function neural network," Omega, Elsevier, vol. 27(3), pages 349-362, June.
    5. Curry, Bruce, 2004. "'Simple' neural networks for forecasting," Omega, Elsevier, vol. 32(2), pages 97-100, April.
    6. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
    7. repec:lan:wpaper:4407 is not listed on IDEAS
    8. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    9. repec:lan:wpaper:4408 is not listed on IDEAS
    10. Pendharkar, Parag C., 2002. "A computational study on the performance of artificial neural networks under changing structural design and data distribution," European Journal of Operational Research, Elsevier, vol. 138(1), pages 155-177, April.
    11. repec:lan:wpaper:4839 is not listed on IDEAS
    12. repec:lan:wpaper:4535 is not listed on IDEAS
    13. Pendharkar, Parag C., 2001. "An empirical study of design and testing of hybrid evolutionary-neural approach for classification," Omega, Elsevier, vol. 29(4), pages 361-374, August.
    14. Curry, B. & Morgan, P.H., 2006. "Model selection in Neural Networks: Some difficulties," European Journal of Operational Research, Elsevier, vol. 170(2), pages 567-577, April.
    15. Murugan Anandarajan & Picheng Lee & Asokan Anandarajan, 2001. "Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 69-81, June.
    16. Gupta, Jatinder N. D. & Sexton, Randall S., 1999. "Comparing backpropagation with a genetic algorithm for neural network training," Omega, Elsevier, vol. 27(6), pages 679-684, December.
    17. Geraint Johnes, 2005. "‘Don’t Know Much About History…’: Revisiting the Impact of Curriculum on Subsequent Labour Market Outcomes," Bulletin of Economic Research, Wiley Blackwell, vol. 57(3), pages 249-271, July.

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