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On the overtraining phenomenon of backpropagation neural networks

Author

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  • Tzafestas, S.G.
  • Dalianis, P.J.
  • Anthopoulos, G.

Abstract

A very important subject for the consolidation of neural networks is the study of their capabilities. In this paper, the relationships between network size, training set size and generalization capabilities are examined. The phenomenon of overtraining in backpropagation networks is discussed and an extension to an existing algorithm is described. The extended algorithm provides a new energy function and its advantages, such as improved plasticity and performance along with its dynamic properties, are explained. The algorithm is applied to some common problems (XOR, numeric character recognition and function approximation) and simulation results are presented and discussed.

Suggested Citation

  • Tzafestas, S.G. & Dalianis, P.J. & Anthopoulos, G., 1996. "On the overtraining phenomenon of backpropagation neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 40(5), pages 507-521.
    • Handle: RePEc:eee:matcom:v:40:y:1996:i:5:p:507-521
    DOI: 10.1016/0378-4754(95)00003-8
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    Cited by:

    1. El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
    2. Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
    3. Tzafestas, E.S. & Nikolaidou, A. & Tzafestas, S.G., 2000. "Performance evaluation and dynamic node generation criteria for ‘principal component analysis’ neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 145-156.

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