In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical inte...In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.展开更多
直接探测多普勒测风激光雷达系统大多采用基于Fabry-Perot(F-P)标准具的条纹技术和边缘技术测量多普勒频移。但激光器频率漂移会使得透过率曲线的估计出现一定误差,为了抑制其影响,提出了一种基于谱估计的F-P标准具透过率曲线参数估计...直接探测多普勒测风激光雷达系统大多采用基于Fabry-Perot(F-P)标准具的条纹技术和边缘技术测量多普勒频移。但激光器频率漂移会使得透过率曲线的估计出现一定误差,为了抑制其影响,提出了一种基于谱估计的F-P标准具透过率曲线参数估计方法。首先将标准具全量程扫描结果进行重构得到观测矩阵。之后使用MUSIC算法得到透过率曲线的伪谱。对伪谱进行谱峰搜索后,将得到的谱峰位置进行线性拟合得到自由谱间距。然后将上步的拟合结果代入透过率函数,利用非线性最小二乘法对其他参数进行估计。在仿真分析的基础上,采用真实测量数据进行了实验验证。结果表明所提出的方法在扫描步点间隔达到50、信噪比大于10 d B的情况下,估计差小于1%。该算法扫描时间短,估计误差小,具有实际应用价值。展开更多
基金supported by the National Natural Science Foundation of China(No.92370117)the CAS Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.
文摘直接探测多普勒测风激光雷达系统大多采用基于Fabry-Perot(F-P)标准具的条纹技术和边缘技术测量多普勒频移。但激光器频率漂移会使得透过率曲线的估计出现一定误差,为了抑制其影响,提出了一种基于谱估计的F-P标准具透过率曲线参数估计方法。首先将标准具全量程扫描结果进行重构得到观测矩阵。之后使用MUSIC算法得到透过率曲线的伪谱。对伪谱进行谱峰搜索后,将得到的谱峰位置进行线性拟合得到自由谱间距。然后将上步的拟合结果代入透过率函数,利用非线性最小二乘法对其他参数进行估计。在仿真分析的基础上,采用真实测量数据进行了实验验证。结果表明所提出的方法在扫描步点间隔达到50、信噪比大于10 d B的情况下,估计差小于1%。该算法扫描时间短,估计误差小,具有实际应用价值。
