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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
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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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