中北大学学报（自然科学版）Issue(6)：699-703,5.DOI:10.3969/j.issn.1673-3193.2014.06.016
K-均值聚类模糊逻辑数据融合改进算法研究
An Improved Fusion Method of Fuzzy Logic Based on K-Mean Clustering
摘要
Abstract
For the problem of large deviation of data fusion based on weighted fuzzy logic algorithm in wireless sensor networks ,a new method is proposed .First to eliminate the flawed data through analysis of initial data using the idea of K-mean clustering ,and to revise the weighting factors of weighted fuzzy logic algorithm with the rest authentic data ,and then intergrate all data by means of weighted method to get the final fusion re-sult .Experimental results show that this method can achieve higher integration accuracy compared with the other same fusion methods .关键词
无线传感网络/数据融合/模糊逻辑/K-均值聚类Key words
wireless sensor networks/data fusion/fuzzy logic algorithm/K-mean clustering分类
信息技术与安全科学引用本文复制引用
王峰,籍锦程,聂百胜..K-均值聚类模糊逻辑数据融合改进算法研究[J].中北大学学报（自然科学版）,2014,(6):699-703,5.基金项目
山西省自然科学基金资助项目（2012011013-4）；山西省高等学校留学回国人员科研资助项目（晋教外［2011］号）；山西省普通高校特色重点学科建设资助项目 （）
