An Improved BP Algorithm over Out-of-order Streams for Big Data

被引：0

作者：

Wang, Kun ^{[1
]}

Zhuo, Linchao ^{[1
]}

Lu, Heng ^{[1
]}

Guo, Huang ^{[1
]}

Xu, Lili ^{[1
]}

Zhang, Yuhua ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Minist Educ, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China

来源：

2013 8TH INTERNATIONAL ICST CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM) | 2013年

关键词：

BP Algorithm; Out-of-order Streams; Machine Learning; Big Data;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Due to the 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.

引用

页码：840 / 845

页数：6

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