Referenced Items are not available for this document.
The output weight optimization-hidden weight optimization (OWO-HWO) feedforward network training algorithm alternately solves linear equations for output weights and reduces a separate hidden layer error function with respect to hidden layer weights. Here, a new hidden layer error function is proposed which de-emphasizes net function errors that correspond to saturated activation function values. In addition, an adaptive learning rate based on the local shape of the error surface is used in hidden layer training. Faster learning convergence is experimentally verified.
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
- Page(s):
- 1034 - 1038 vol.2
- ISSN :
- 1058-6393
- Print ISBN:
- 0-7803-7576-9
- INSPEC Accession Number:
- 7792648
- Conference Location :
- Pacific Grove, CA, USA
- Digital Object Identifier :
- 10.1109/ACSSC.2002.1196941
- Product Type:
- Conference Publications
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