激光与光电子学进展, 2021, 58 (6): 0629001, 网络出版: 2021-03-06
基于机器学习的椭球颗粒群消光系数预测
Prediction of Extinction Coefficient of Ellipsoid Particle Group Based on Machine Learning
机器视觉 消光系数 反常衍射近似 椭球颗粒 machine vision extinction coefficient anomalous diffraction approximation ellipsoid particle
摘要
通过消光系数反演椭球颗粒群的几何参数是颗粒测量领域的重要问题,传统基于进化算法的反演技术需多次数值积分求解消光系数,效率较低。针对该问题,提出了一种基于机器学习的计算方法。首先,参数化表达颗粒群的粒径与形状;然后,基于反常衍射近似理论建立椭球形颗粒消光系数的训练及测试数据集;最后,用多层感知器人工神经网络实现颗粒群参数与消光系数之间的映射,并研究了神经元数目、波长、颗粒群分布模型等因素对预测精度及效率的影响。实验结果表明,当隐藏层神经元数目为20时,预测平均误差低于0.05%,单机预测时间约为0.6 μs。该技术提供了一种高效准确的消光系数计算工具,进一步结合进化算法有望实现球形及椭球颗粒群粒径与形状参数的实时反演。
Abstract
The inversion of the geometric parameters of the ellipsoidal particle group through the extinction coefficient is an important problem in the field of particle measurement. The traditional inversion technology based on evolutionary algorithm requires multiple numerical integration to solve the extinction coefficient, which is low in efficiency. To solve this problem, an acceleration method based on machine learning is proposed in this work. First, the particle size and shape are expressed parametrically; second, the training and testing datasets of ellipsoidal particle extinction coefficient are established based on the anomalous diffraction approximation theory; finally, the mapping between particle parameters and extinction coefficient is realized by using multilayer perceptron artificial neural network, and the effects of the number of neurons, wavelength, particle group distribution model and other factors on the prediction accuracy and efficiency are studied. Experimental results show that when the number of hidden layer neurons is 20, the average prediction error is less than 0.05%, and the single machine prediction time is about 0.6 μs. The technology provides an efficient and accurate extinction coefficient calculation tool. With further employment of evolutionary algorithms, it is expected to realize the real-time inversion of spherical and ellipsoidal particle size and shape parameters.
张晓浩, 陈功叶, 李浩淼, 彭浩辰, 曹兆楼. 基于机器学习的椭球颗粒群消光系数预测[J]. 激光与光电子学进展, 2021, 58(6): 0629001. Zhang Xiaohao, Chen Gongye, Li Haomiao, Peng Haochen, Cao Zhaolou. Prediction of Extinction Coefficient of Ellipsoid Particle Group Based on Machine Learning[J]. Laser & Optoelectronics Progress, 2021, 58(6): 0629001.