核电厂环境辐射监测传感器网络中缺失值的粒子群算法-最小二乘支持向量机估计算法  

PSO-LSSVM Based Missing Data Imputation Algorithmfor Wireless Sensor Network for Nuclear Power Plants' Environmental Radiation Monitor

作　　者：高雨晨[1] 唐耀庚[2] 

机构地区：[1]广东工业大学自动化学院,广州510006 [2]南华大学电气工程学院,衡阳421001

出　　处：《核电子学与探测技术》2014年第12期1508-1513,共6页Nuclear Electronics & Detection Technology

摘　　要：传感器节点监测数据缺失会影响核电厂外围环境辐射监测的连续性,必须对缺失数据进行准确估计。提出一种基于最小二乘支持向量机(LSSVM)的监测数据缺失值估计算法,采用粒子群算法(PSO)确定模型参数的优化组合,根据核电厂外围环境(剂量率变化特点,利用节点的历史监测数据和相邻节点当前监测数据构造样本空间,对传感器节点监测数据缺失值进行估计。用实际数据进行的实验结果表明,所提出的估计算法的最大相对估计误差为3%,相关系数为0.926375,估计精度远高于基于BP神经网络模型的估计算法,也优于采用GA优化参数的LSSVM估计算法。Sensor nodes＇ monitoring data missing can cause damage to environment radiation continuous monitoring for nuclear power plant,the missing values should be estimated as accurately as possible. A missing data imputation algorithm based on least squares support vector machine（ LSSVM） is proposed,where the optimum parameter set is found using particle swarm optimization algorithm（ PSO）. This algorithm imputes missing data utilizing node＇s previous monitoring data and neighbor node＇s current monitoring data jointly. Experimental results show that the proposed algorithm can impute the missing γ dose rate data with a maximum relative error of3% and correlation coefficient of 0. 955378,much better than the GA-LSSVM and the feed-forward network working with the same data set.

关 键 词：环境辐射监测 无线传感网(WSN) 缺失值 估计 粒子群优化最小二乘支持向量机 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]