Improving the convergence of the back-propagation algorithm
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Improving the error backpropagation algorithm with a modified error function
IEEE Transactions on Neural Networks
Global artificial bee colony algorithm for boolean function classification
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
A new back-propagation neural network optimized with cuckoo search algorithm
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
A new cuckoo search based levenberg-marquardt (CSLM) algorithm
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
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A study on improving training efficiency of Artificial Neural Networks algorithm was carried out throughout many previous papers. This paper presents a new approach to improve the training efficiency of back propagation neural network algorithms. The proposed algorithm (GDM/AG) adaptively modifies the gradient based search direction by introducing the value of gain parameter in the activation function. It has been shown that this modification significantly enhance the computational efficiency of training process. The proposed algorithm is generic and can be implemented in almost all gradient based optimization processes. The robustness of the proposed algorithm is shown by comparing convergence rates and the effectiveness of gradient descent methods using the proposed method on heart disease data.