An Equalized Error Backpropagation Algorithm for The On-Line Training of Multilayer Perceptrons  

Oleh:

Weymaere, N. ; Martens, J.-P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 3 (2002), page 532-541.
Topik: multilayer networks; backpropagation; algorithm; on line trainig; multilayer perceptrons

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The error backpropagation (EBP) training of a multilayer perceptron (MLP) may require a very large number of training epochs. Although the training time can usually be reduced considerably by adopting an on - line training paradigm, it can still be excessive when large networks have to be trained on lots of data. In this paper, a new on - line training algorithm is presented. It is called equalized EBP (EEBP), and it offers improved accuracy, speed, and robustness against badly scaled inputs. A major characteristic of EEBP is its utilization of weight specific learning rates whose relative magnitudes are derived from a priori computable properties of the network and the training data.

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