计算机工程与应用2016，Vol.52Issue(23)：229-235,7.DOI:10.3778/j.issn.1002-8331.1502-0121
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
摘要
Abstract
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Key words
Levy flight/adaptive/particle swarm optimization/infrared gas sensor/temperature compensation分类
信息技术与安全科学引用本文复制引用
毛琪波,余震虹..改进的粒子群算法在传感器温度补偿中的应用[J].计算机工程与应用,2016,52(23):229-235,7.基金项目
江苏省气体传感器工程技术研究中心（No.BM2010645）。 （）
