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The Evolution of a Feedforward Neural Network trained under Backpropagation

  • D. McLean
  • Z. Bandar
  • J. D. O’Shea
Conference paper

Abstract

This paper presents a theoretical and empirical analysis of the evolution of a feedforward neural network (FFNN) trained using backpropagation (BP). The results of two sets of experiments axe presented which illustrate the nature of BP’s search through weight space as the network learns to classify the training data. The search is shown to be driven by the initial values of the weights in the output layer of neurons.

Keywords

Output Layer Feedforward Neural Network Weight Space Random Weight Initialisation Random Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • D. McLean
    • 1
  • Z. Bandar
    • 1
  • J. D. O’Shea
    • 1
  1. 1.The Intelligent Systems GroupThe Manchester Metropolitan UniversityManchesterUK

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