Research Article
An Improved BP Algorithm over Out-of-order Streams for Big Data
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694712, author={Kun Wang and Linchao Zhuo and Heng Lu and Huang Guo and Lili Xu and Yuhua Zhang}, title={An Improved BP Algorithm over Out-of-order Streams for Big Data}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={bp algorithm out-of-order streams machine learning big data}, doi={10.1109/ChinaCom.2013.6694712} }- Kun Wang
Linchao Zhuo
Heng Lu
Huang Guo
Lili Xu
Yuhua Zhang
Year: 2013
An Improved BP Algorithm over Out-of-order Streams for Big Data
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694712
Abstract
Due tothe difficulty of getting the association rules over out-of-order streams for big data, a new improved BP algorithm based on dynamic adjustment is proposed. We firstly use a dynamic adaptive structural adjustment mechanism to change the network training structure according to the environmental requirements, which can automatically remove invalid training node, and optimize the iterative training process. Secondly, we adjust three factors (i.e. learning index, momentum factor and scaling factor) during the learning process to speed up the learning response, and to enhance the stability of the network. Simulation results show that compared with traditional BP algorithm, this algorithm can get more convergence times,the convergence rate can be improved effectively, and finally obtain the association rules over out-of-order data streams.