[1]鞠训光,邵晓根,鲍 蓉,等.Hadoop下并行BP神经网络骆马湖水质分类[J].南京师大学报(自然科学版),2014,37(01):52.
　Ju Xunguang,Shao Xiaogen,Bao Rong,et al.Based on Parallel BP Neural Network of Classification on Water Quality of Luoma Lake Under Hadoop[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(01):52.
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第37卷
期数:
2014年01期
页码:
52
栏目:
计算机科学
出版日期:
2014-03-30

文章信息/Info

Title:
Based on Parallel BP Neural Network of Classification on Water Quality of Luoma Lake Under Hadoop
作者:
鞠训光1; 邵晓根1; 鲍 蓉1; 徐德兰2; 王海鹰1
(1.徐州工程学院信电工程学院,江苏 徐州 221111) (2.徐州工程学院环境工程学院,江苏 徐州 221111)
Author(s):
Ju Xunguang1; Shao Xiaogen1; Bao Rong1; Xu Delan2; Wan Haiying1
(1.School of Information and Electrical Engineering,Xuzhou Institute of Technology,Xuzhou 221111,China) (2.School of Environmental Engineering,Xuzhou Institute of Technology,Xuzhou 221111,China)
关键词:
骆马湖水质分类; Hadoop; 并行BP神经网络
Keywords:
water quality of Luoma Lake; Hadoop; parallel BP neural network
分类号:
TP391
文献标志码:
A
摘要:
研究借助云的计算向数据迁移机制及MapReduce并行处理海量数据的优势,解决BP神经网络在处理大规模样本数据时计算量大、网络训练时间长的瓶颈问题.构建了影响骆马湖水质的多污染因素评价网络模型,在Hadoop下应用并行BP网络算法,实现了对骆马湖水质分类挖掘,挖掘分析结果对骆马湖水质优化及生态修复具有决策支持性意义.
Abstract:
Research the advantage of using the mechanism of computing to data migration and MapReduce parallel processing of massive data,to solve the bottlenecks problem on large amount of computing and network training time when the BP neural network in dealing with a large sample data.Its constructed water quality evaluation model based on the pollution influence factors of Luoma Lake and mined the water quality classification of Luoma Lake by applied the parallel BP algorithm under Hadoop.Mining analysis results is meaningful of decision support for the water quality optimization and ecological remediation of Luoma Lake.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-07-15.
基金项目:科技部国家中小企业创新基金(11C26213204533)、徐州市科技计划(XF11C052)、住房与城乡建设部科学技术计划(2011-K6-27).
通讯联系人:鞠训光,博士,副教授,研究方向:智能计算、数据挖掘、云计算.E-mail:375768447@qq.com
更新日期/Last Update: 2014-03-30