Journal of Automation and Information Sciences
年間 12 号発行
ISSN 印刷: 1064-2315
ISSN オンライン: 2163-9337
SJR:
0.173
SNIP:
0.588
CiteScore™::
2
Indexed in
Examining Bp Modification in Neural Arrays of Different Dimensionalities
巻 28,
発行 5-6, 1996,
pp. 152-158
DOI: 10.1615/JAutomatInfScien.v28.i5-6.180
要約
The backpropagation neural network is the most popular network architecture. A better weight update for this learning rule would be possible if we could compensate for future changes to these weights in earlier layers. To do so, we address some modifications of the backpropagation learning algorithm that use the expected value of the source. These modifications are examined in neural arrays of different dimensionalities by means of computer simulation. A method of using these networks as pattern classifiers is proposed and simulated.
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