Abstract
Artificial neural network is proved to be an effective algorithm for dealing with recognition, regression and classification tasks. At present a number of neural network implementations have been developed, for example Hamming network, Grossberg network, Hopfield network and so on. Among these implementations, back propagation neural network (BPNN) has become the most popular one due to its sensational function approximation and generalization abilities. However, in the current big data researches, BPNN, as a both data intensive and computational intensive algorithm, its efficiency has been significantly impacted. Therefore, this paper presents a parallel BPNN algorithm based on data separation in three distributed computing environments including Hadoop, HaLoop and Spark. Moreover to improve the algorithm performance in terms of accuracy, ensemble techniques have been employed. The algorithm is firstly evaluated in a small-scale cluster. And then it is further evaluated in a commercial cloud computing environment. The experimental results indicate that the proposed algorithm can improve the efficiency of BPNN with guaranteeing its accuracy.
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