基于无阈值递归图的改进2D-BLS褐潮藻细胞密度预测  

Prediction of Brown Tide Algae Cell Density Based on Improved 2D-BLS with Unthresholded Recurrence Plots

作　　者：朱奇光[1] 李享 刘俊飞 董志阳 陈颖[2] Zhu Qiguang;Li Xiang;Liu Junfei;Dong Zhiyang;Chen Ying(Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province,School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China;Hebei Province Key Laboratory of Test/Measurement Technology and Instrument,School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China)

机构地区：[1]燕山大学信息科学与工程学院河北省特种光纤与光纤传感器重点实验室,河北秦皇岛066004 [2]燕山大学电气工程学院测试计量技术与仪器河北省重点实验室,河北秦皇岛066004

出　　处：《光学学报》2024年第23期261-271,共11页Acta Optica Sinica

基　　金：国家自然科学基金(62275228);河北省重点研发计划项目(19273901D,20373301D);河北省自然科学基金(D2024203002)。

摘　　要：为了克服荧光法褐潮藻细胞密度预测中三维荧光光谱法采集数据速度相对较慢而发光二极管(LED)诱导荧光光谱法所获得的一维光谱数据光谱特征较少等不足,采用LED诱导荧光光谱法实现一维光谱数据快速获取,并对其进行深入挖掘和数据升维。针对传统递归图算法易受人为因素影响的缺点,提出无阈值递归图,采用Jaccard相似系数来选取无阈值递归图相空间重构参数,从而实现光谱数据升维;采用弹性网络回归算法的方式来代替原来的输入权重正则化方式,从而达到稀疏性和稳定性的双重要求;引入左右投影矩阵建立二维宽度学习系统。通过对提出的预测模型进行性能分析可以发现,相比于其他对比模型,所提模型的性能表现最佳,其R^(2)、RMSE和MAE在训练集和测试集上的平均值分别为0.9994、0.00594、0.00355,同时,模型在训练集和测试集的时间指标也均取得了最好的效果,证明该模型可以准确、快速地实现褐潮藻细胞密度预测。Objective In recent years,frequent outbreaks of brown tide in the offshore waters of the Bohai Sea,primarily caused by the overgrowth of Aureococcus anophagefferens,the causative species of brown tide,have significantly disrupted the marine ecosystem and caused severe economic losses.Therefore,developing effective methods to detect and predict Aureococcus anophagefferens cell density is essential for brown tide monitoring and control.Fluorescence spectroscopy,a widely used method for detecting algal cell density,offers advantages such as non-destructive testing,high sensitivity,low interference,and simple preprocessing.Specifically,LED-induced fluorescence technology facilitates the rapid acquisition of one-dimensional fluorescence spectra;however,the spectral intensity data points from a single sample are far fewer than those from three-dimensional fluorescence spectra.Recurrence plots can expand spectral data dimensions through phase space reconstruction,increasing the data volume of individual samples.However,the original recurrence plot algorithm is susceptible to the influence of human bias.In fluorescence analysis,nonlinear models are often used to mitigate the inner filter effects.Among these,the broad learning system(BLS)is advantageous due to its simple structure,low computational requirements,and small sample size demands.Nevertheless,the original BLS struggles with direct twodimensional data input.To address these issues,we propose using unthresholded recurrence plots and an improved twodimensional BLS(2D-BLS)to predict brown tidal algal cell density.Methods We focus on Aureococcus anophagefferens as the causative species of brown tides and propose an improved 2D-BLS for predicting brown tide cell density,incorporating unthresholded recurrence plots.LED-induced fluorescence spectroscopy is employed for rapid one-dimensional spectral data collection,and the unthresholded recurrence plot is used to enrich the data volume set by expanding the dimensionality of the spectral data.The Jaccard similarity coef

关 键 词：褐潮藻细胞密度预测 LED诱导荧光光谱法 二维宽度学习系统 无阈值递归图 弹性网络回归 左右投影矩阵 

分 类 号：X834[环境科学与工程—环境工程]