摘要
针对无线传感网(WSN)数据融合中基于模糊逻辑的加权融合算法融合结果误差偏大的问题,提出了一种基于K-均值聚类的改进的模糊逻辑加权融合算法.首先运用K-均值聚类的思想分析收集到的原始误差数据,去除算法认为不可靠的数据,用余下的有效数据对修正模糊逻辑算法求得加权因子,并与节点测量数据加权平均求值,得到最终融合值.实验证明:通过与其它同类的加权融合算法比较,该改进算法的融合精度更高,效果更好.
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- suit. Experimental results show that this method can achieve higher integration accuracy compared with the other same fusion methods.
出处
《中北大学学报（自然科学版）》
CAS
北大核心
2014年第6期699-703,共5页
Journal of North University of China(Natural Science Edition)
基金
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
关键词
无线传感网络
数据融合
模糊逻辑
K-均值聚类
wireless sensor networks
data fusion
fuzzy logic algorithm
K-mean clustering
