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Enhanced robustness of multilayer perceptron training

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2 Author(s)
W. H. Delashmit ; Lockheed Martin Missiles & Fire Control, Dallas, TX, USA ; M. T. Manry

Due to the chaotic nature of multilayer perceptron training, training error usually fails to be a monotonically non-increasing function of the number of hidden units. An initialization and training methodology is developed to significantly increase the probability that the training error is monotonically non-increasing. First a structured initialization generates the random weights in a particular order. Second, larger networks are initialized using weights from smaller trained networks. Lastly, the required number of iterations is calculated as a function of network size.

Published in:

Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on  (Volume:2 )

Date of Conference:

3-6 Nov. 2002