作　　者： ; ; ; ; ; ;

机构地区： 安庆师范学院物理与电气工程学院

出　　处： 《传感技术学报》 2012年第4期472-477,共6页

摘　　要： 针对传感器的测量精度受温度影响较大问题,提出了一种基于云粒子群-最小二乘支持向量机(CMPSO-LSSVM)的温度补偿方法。云粒子群算法(CMPSO)将云模型算法应用于粒子群优化(PSO)算法的收敛机制,具有寻优精度高的特点。CMPSO算法对LSSVM的参数进行优化选择,建立CMPSO-LSSVM传感器温度补偿模型。将该模型应用于振弦式传感器的温度补偿,通过实验证明了该温度补偿方法优于当前其他主要方法。 The precision of sensor is affected greatly by temperature, and a new method is put forward for sensor temperature compensation based on Cloud Model Particle Swarm Optimization-Least Square Support Vector Machine （CMPSO-LSSVM）. Cloud model particle swarm optimization （CMPSO）algorithm is proposed when cloud model algorithm was introduced into the convergence process of PSO algorithm. The simulations prove the CMPSO has better optimization performance than the other main PSOs. The CMPSO searches parameters for LSSVM and established the temperature compensation model of vibrating-wire sensor. This method improves the temperature stability and its accuracy is more better than the other main methods, which has been proved through experiments.

关 键 词： 云模型 粒子群优化 最小二乘支持向量机 温度补偿

领　　域： [自动化与计算机技术] [自动化与计算机技术]