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2004

Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques

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Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, , and learning rate, , with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm. Keywords-- Backpropagation, Dynamic Adaptation Methods, Local Adaptive Techniques, Neural networks.
Z. Zainuddin, N. Mahat, Y. Abu Hassan
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where IJIT
Authors Z. Zainuddin, N. Mahat, Y. Abu Hassan
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