Computer Engineering and Applications ›› 2016, Vol. 52 ›› Issue (23): 229-235.

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Improved PSO and its application to sensor temperature compensation

MAO Qibo, YU Zhenhong   

  1. College of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2016-12-01 Published:2016-12-20

改进的粒子群算法在传感器温度补偿中的应用

毛琪波,余震虹   

  1. 江南大学 物联网工程学院,江苏 无锡 214122

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%.

Key words: Levy flight, adaptive, particle swarm optimization, infrared gas sensor, temperature compensation

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。

关键词: Levy flight, 自适应, 粒子群优化, 红外气体传感器, 温度补偿