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为了提高光传感器参数的标定准确性,本文提出一种粒子群优化算法优化相关向量机参数的光传感器参数标定算法(PSO-RVM)。首先收集光传感器参数的数据,并进行归一化处理,然后建立光传感器参数选择训练样本和测试样本,并输入到相关向量机建立光传感器参数标定模型,采用粒子群算法对相关向量机参数进行优化,最后采用光传感器参数标定实验进行性能分析。结果表明,PSO-RVM可以描述光传感器参数的变化特点,提高了光传感器参数标定精度,并且标定速度可以满足实际应用要求。
In order to improve the calibration accuracy of the optical sensor parameters, a particle swarm optimization (PSO-RVM) algorithm is proposed to optimize the parameters of the SVM. Firstly, the data of the optical sensor parameters are collected and normalized. Then, the optical sensor parameters are selected to train the training samples and the test samples, and then input to the relevant vector machines to establish the optical sensor parameter calibration model. Particle swarm optimization Optimization, and finally using optical sensor parameter calibration experiment for performance analysis. The results show that PSO-RVM can describe the characteristics of the optical sensor parameters and improve the calibration accuracy of the optical sensor parameters, and the calibration speed can meet the practical application requirements.