Abstract
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.
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Nawi, N.M., Ghazali, R., Salleh, M.N.M. (2010). The Development of Improved Back-Propagation Neural Networks Algorithm for Predicting Patients with Heart Disease. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_41
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DOI: https://doi.org/10.1007/978-3-642-16167-4_41
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