南京师大学报（自然科学版）Issue(1)：52-56,5.
Hadoop下并行BP神经网络骆马湖水质分类
Based on Parallel BP Neural Network of Classification on Water Quality of Luoma Lake Under Hadoop
摘要
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.关键词
骆马湖水质分类/Hadoop/并行BP神经网络Key words
water quality of Luoma Lake/Hadoop/parallel BP neural network分类
信息技术与安全科学引用本文复制引用
鞠训光,邵晓根,鲍蓉,徐德兰,王海鹰..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,(1):52-56,5.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) （11C26213204533）
