改进的粒子群算法在传感器温度补偿中的应用  

Improved PSO and its application to sensor temperature compensation

作　　者：毛琪波[1] 余震虹[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机工程与应用》2016年第23期229-235,共7页Computer Engineering and Applications

基　　金：基金项目:江苏省气体传感器工程技术研究中心(No.BM2010645)

摘　　要：针对红外气体传感器测量精度受环境温度影响较大的问题,提出了一种基于嵌入自适应Levy变异免疫粒子群-最小二乘支持向量机(ALIPSO-LSSVM)的温度补偿算法。ALIPSO算法引入Levy flight对子代粒子进行自适应变异,确保粒子多样性,并在每次迭代之前,采用相对基学习方法初始化粒子群,提高算法的收敛速度。通过5个基准测试函数对ALIPSO算法进行性能评价,仿真结果表明该算法收敛速度较快、精度高,且具有较强的全局搜索能力。利用ALIPSO算法对LS-SVM的参数进行优化,并将该混合算法应用于红外气体传感器温度补偿,数值仿真结果表明采用该算法可将补偿结果的相对误差控制在6%范围内。Focused on the issue that the precision of infrared gas sensor is affected greatly by temperature, a new method is put forward for sensor temperature compensation based on Adaptive Levy mutation Immune Particle Swarm Optimization-Least Square Support Vector Machine（ALIPSO-LSSVM）. Levy flight is introduced in the adaptive mutation of offspring to ensure the diversity, and opposition-based learning is used to initialize the particle swarm to improve the convergence speed in the ALIPSO algorithm. Performance comparison with other PSOs is made through 5 benchmark test functions. Based on the ALIPSO, the optimum parameter selection of Least Squares SVM（LS-SVM）is studied, and the temperature compensation model of infrared gas sensor is established, the numerical simulation results show the relative error can be controlled within 6%.

关 键 词：LEVY FLIGHT 自适应 粒子群优化 红外气体传感器 温度补偿 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]