TY - JFULL AU - Z. Zainuddin and N. Mahat and Y. Abu Hassan PY - 2007/2/ TI - Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques T2 - International Journal of Computer and Information Engineering SP - 183 EP - 187 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/10808 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 1, 2007 N2 - 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, α, and learning rate, η, 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. ER -