基于数据挖掘的激光传感器参数优化数学模型  

A mathematical model for optimizing laser sensor parameters based on data mining

作　　者：王婷 仝延增 WANG Ting;TONG Yanzeng(College of Mathematics and Science,Handan University,Handan Hebei 056005,China)

机构地区：[1]邯郸学院数理学院,河北邯郸056005

出　　处：《激光杂志》2025年第3期210-215,共6页Laser Journal

基　　金：河北省科技厅项(No.18211844)。

摘　　要：为提升激光传感器的灵敏度、分辨率等指标,构建基于数据挖掘的激光传感器参数优化数学模型。获得激光传感器测量范围内的目标物体移动距离以及不同夹角等动态参数,然后使用EMD算法挖掘激光传感器测量时动态数据的特征向量,RBF神经网络根据特征向量判别当前激光传感器参数是否偏离正常状态下的理论值,建立激光传感器参数优化数学模型,并对偏离正常状态的激光传感器参数进行优化。实验表明:该方法可准确判别激光传感器发射器到物像基准距离是否存在偏离情况,并可有效对激光传感器参数进行优化,灵敏度提升了15.29%,应用效果好。To improve the sensitivity,resolution,and other indicators of laser sensors,a mathematical model for optimizing laser sensor parameters based on data mining is constructed.,Obtain dynamic parameters such as the moving distance and different angles of the target object within the measurement range of the laser sensor,and then use the EMD algorithm to mine the feature vectors of the dynamic data during the measurement of the laser sensor.The RBF neural network judges whether the current laser sensor parameters deviate from the theoretical values in the normal state based on the feature vectors,establishes a mathematical model for optimizing the laser sensor parameters,and optimizes the laser sensor parameters that deviate from the normal state.The experiment shows that this method can accurately distinguish whether there is a deviation in the distance between the laser sensor emitter and the object image reference,and can effectively optimize the parameters of the laser sensor.The sensitivity has been improved by 15.29%,and the application effect is good.

关 键 词：数据挖掘 激光传感器 参数优化 数学模型 EMD算法 RBF神经网络 

分 类 号：TN279[电子电信—物理电子学]