Fast Learning for Big Data Using Dynamic Function

IAES International Conference on Electrical Engineering, Computer Science and Informatics

电工学;计算机科学

Alwajeeh, T.^1 ; Alharthi, A.F.^3 ; Rahmat, R.F.^2 ; Budiarto, R.^3

Dept. of Computer Science and Engineering, College of CSandIT, Albaha University, P.O. Box 1988, Albaha, Saudi Arabia^1

Department of Information Technology, Faculty of Computer Science and Information Technology, University of Sumatera Utara, Medan, Indonesia^2

Dept. of Computer Information System, College of CSandIT, Albaha University, P.O. Box 1988, Albaha, Saudi Arabia^3

关键词: Back propagation neural networks;    Dynamic functions;    Fast convergence;    Momentum factor;    Parity problems;    Sigmoid function;    Standard algorithms;    Training process;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/190/1/012015/pdf
DOI  :  10.1088/1757-899X/190/1/012015

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper discusses an approach for fast learning in big data. The proposed approach combines momentum factor and training rate, where the momentum is a dynamic function of the training rate in order to avoid overshoot weight to speed up training time of the back propagation neural network engine. The two factors are adjusted dinamically to assure the fast convergence of the training process. Experiments on 2-bit XOR parity problem were conducted using Matlab and a sigmoid function. Experiments results show that the proposed approach signifcantly performs better compare to the standard back propagation neural network in terms of training time. Both, the maximum training time and the minimum training time are significantly faster than the standard algorithm at error threshold of 10-5.