Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

A genetic Gaussian process regression model based on memetic algorithm

Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

基于SARIMA和SVR组合模型的转向架系统寿命评估

随着地铁运营时间和里程的增加,地铁车辆逐渐接近其理论寿命,为确保车辆运行安全性,需对其重要子系统进行健康状态及剩余寿命评估。文中选取车辆转向架系统作为研究对象,提出了一种基于协方差优选法的季节性回归移动平均(SARIMA)和支持向量回归(SVR)的组合模型对转向架寿命进行评估。首先,将车辆转向架系统历史故障率转化为健康指数,然后基于协方差优选法将SARIMA和SVR进行赋权组合,根据转向架系统历史健康指数进行预测,最后建立历史和预测的健康指数与运行时间的数学模型,分析得到转向架系统的剩余寿命。以某地铁车辆转向架系统为例进行算例分析及验证,结果表明组合模型可更准确地预测其健康状态,为有关维修部门开展维修维护策略提供理论依据,估计得出其剩余寿命,为车辆寿命后期退役及延寿决策提供理论数据分析支撑。

基于多模型融合策略的温室番茄光合速率预测方法

温室番茄光合速率的准确预测对于番茄的生长和产量评估具有重要意义。然而,由于温室环境的复杂性和多变性,传统的光合速率预测模型往往难以满足精准预测的需求。因此,为了进一步提高预测模型的准确性和稳定性,本研究提出了一种基于多模型融合策略的温室番茄光合速率预测方法。首先,采集温湿度、光照强度、CO_(2)浓度不同组合下的番茄光合速率,构建样本集,并采用五折交叉验证法(Cross-Validation)对数据进行预处理。以预处理的数据为基础,分别基于粒子群优化支持向量机(PSO-SVR)、布谷鸟优化极限学习机(CS-ELM)和北方苍鹰优化高斯过程回归(NGO-GPR)算法建立番茄光合速率预测模型对光合速率进行初步预测,然后采用Stacking算法通过基于决策树的集成学习模型(XGBoost)组合各基础模型的预测结果,进而实现多模型融合。仿真分析结果表明,与单一预测模型相比,基于多模型融合的光合速率预测模型充分发挥了各基础模型的优势,可以进一步提高光合速率预测的准确性和稳定性,该模型验证集MAE为0.569 7μmol/(m^(2)·s),RMSE为0.721 4μmol/(m^(2)·s)。因此,本文提出的方法在温室作物光合速率预测方面具有一定的优势,可为温室番茄等作物光环境优化调控提供一定的理论基础和技术支撑。

基于管道阻力-流速模型的泥浆输送流速寻优方法

疏浚工程中,管道泥浆最优流速确定多依赖于经验公式,工况变化较大时预测精度不高。采用Durand模型对不同试验工况下的阻力损失进行建模,并基于试验数据对模型常数进行调整,提高Durand模型的预测精度;采用高斯过程回归方法建模,分析训练样本数量对预测结果的影响;提出一种基于管道阻力-流速模型的流速寻优方法,并进行对比试验。结果表明,使用高斯过程回归方法建立的管道阻力模型预测精度更高,可达0.97以上,并可依据管道阻力(浓度)变化实时更新临界流速,从而为疏浚管道泥浆最优流速的确定提供了一种较为有效的寻优方法。

黏土路基回弹模量预测及贝叶斯模型选择研究

【目的】确定黏土路基回弹模量的最优估计模型,实现黏土路基回弹模量的准确预测。【方法】采用贝叶斯高斯过程回归方法,建立了路基土的围压、偏应力、含水率以及干重度与路基回弹模量之间的定量关系,实现了高斯过程回归参数的准确估计与最优影响因子组合的客观选择,在模型的复杂度与拟合程度之间达到了自动平衡。【结果】基于所提出的贝叶斯高斯过程回归方法可准确预测路基的回弹模量,所选最优模型的决定系数(R2)和平均绝对百分误差(RMAPE)分别达到了0.99和1.51%,与全变量模型的预测性能几乎相同。在100次随机试验中,最优模型被选择的比率达到了88%。【结论】所提出的贝叶斯高斯过程回归方法不仅可以通过路基土相关物理力学参数准确预测路基的回弹模量,还可以有效剔除冗余输入变量,在保证模型拟合程度的情况下,降低了模型的复杂度,这对模型的应用与推广具有重要意义。

海上油气井测试射孔峰值压力预测方法

为有效降低海上油气井射孔测试联作施工安全风险,以东海常用套管、油管、射孔枪、射孔弹组合为参考,建立了射孔管柱有限元数值模型。利用控制变量法,结合射孔峰值压力数值模拟结果,分析了总装药量、井筒初始压力、井筒爆炸空间等参数与射孔峰值压力的相关性,在此基础上,通过多元非线性回归建立了射孔峰值压力的计算公式。利用2口井射孔实测压力结果对公式进行验证,射孔峰值压力相对误差分别为6.1%和9.8%,在合理误差范围内。该公式可用于射孔作业前的峰值压力预测,为现场及时作出安全决策提供指导和支持。

Gaussian process regression for prediction and confidence analysis of fruit traits by near-infrared spectroscopy

Detection of fruit traits by using near-infrared(NIR)spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model.Therefore,confidence analysis for a given prediction is required,but this cannot be done using common calibration models of NIR spectroscopy.To address this issue,this paper studied the Gaussian process regression(GPR)for fruit traits detection using NIR spectroscopy.The mean and variance of the GPR were used as the predicted value and confidence,respectively.To show this,a real NIR data set related to dry matter content measurements in mango was used.Compared to partial least squares regression(PLSR),GPR showed approximately 14%lower root mean squared error(RMSE)for the in-distribution test set.Compared with no confidence analysis,using the variance of GPR to remove abnormal samples made GPR and PLSR showed approximately 58%and 10%lower RMSE on the mixed distribution test set,respectively(when the type 1 error rate was set to 0.1).Compared with traditional one-class classification methods,the variance of the GPR can be used to effectively eliminate poorly predicted samples.

Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation,such as artificial intelligence and hybrid models.Recently,the Gaussian process regression(GPR)algorithm has been used successfully in remote sensing and Earth sciences.In this paper,a wavelet-coupled Gaussian process regression(W-GPR)model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia(Algeria).For this purpose,3 years of data(2013-15)have been used in model training while the data of 2016 were used to validate the model.In this work,different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature,relative humidity and extraterrestrial solar radiation on a horizontal surface.The results demonstrated the effectiveness of the new hybrid W-GPR model compared with the classical GPR model in terms of root mean square error(RMSE),relative root mean square error(rRMSE),mean absolute error(MAE)and determination coefficient(R^(2)).

A Gaussian process regression accelerated multiscale model for conduction-radiation heat transfer in periodic composite materials with temperature-dependent thermal properties

Prediction of the coupled conduction-radiation heat transfer in composite materials with periodic structure is important in high-temperature applications of the materials. The temperature dependence of thermal properties complicates the problem. In this work, a multiscale model is proposed for the conduction-radiation heat transfer in periodic composite materials with temperature-dependent thermal properties. Homogenization analysis of the coupled conduction and radiative transfer equations is conducted, in which the temperature dependence of thermal properties is considered. Both the macroscopic homogenized equations and the local unit cell problems are derived. It is proved that the macroscopic average temperature can be used in the unit cell problems for the first-order corrections of the temperature and radiative intensity, and the calculations of effective thermal properties. The temperature dependence of thermal properties only influences the higher-order corrections. A multiscale numerical method is proposed based on the analysis. The Gaussian process (GP) regression is coupled into the multiscale algorithm to build a correlation between thermal properties and temperature for the macroscale iterations and prevent the repetitive solving of unit cell problems. The GP model is updated by additional solutions of unit cell problems during the iteration according to a variance threshold. Numerical simulations of conduction-radiation heat transfer in composite with isotropic and anisotropic periodic structures are used to validate the proposed multiscale model. It is found that the accuracy and efficiency of the multiscale method can be guaranteed by using a proper variance threshold for the GP model. The multiscale model can provide both the average temperature and radiative intensity fields and their detailed fluctuations due to the local structures.

基于核自构建-高斯过程回归的锂离子电池剩余使用寿命预测

高斯过程回归是锂离子电池剩余使用寿命的有效预测方法之一,其中核函数的选择对预测结果有着重要影响。对此,提出了一种自回归核自构建高斯过程回归的锂离子电池剩余寿命预测框架,可结合同型号电池的历史容量退化规律,自动构建出合适的组合核函数。通过与不同的机器学习方法及不同核函数比较,所提方法可在电池退化早期做出长期且准确的电池健康状态退化趋势预测,预测寿命均方根误差小于1%,相对误差小于6%,置信区间也更为集中,证明了所提方法能够有效提高电池剩余使用寿命的长期预测精度。

基于高斯过程回归的机翼/短舱一体化气动优化

为了解决机翼/短舱一体化气动设计的高维非线性优化问题,基于高斯过程回归(GPR)模型提出新型优化设计方法.采用类别形状函数变换(CST)方法对机翼/短舱一体化构型中的翼型进行几何参数化建模;通过控制机翼形状参数、短舱形状参数和短舱安装参数实现机翼/短舱构型变形,该参数化建模过程共计包含50个设计参数.通过GPR模型构建机翼/短舱设计参数与气动性能之间的代理模型,并采用贝叶斯优化(BO)算法实现代理模型的自更新和最优气动外形的获取.结果表明:优化后一体化构型的阻力系数下降了10.95%,通过流场分析发现机翼外形和短舱外形的优化改善了表面流场结构,短舱安装位置的优化减弱了机翼和短舱间的气动干扰.

基于GPR代理模型和GA-APSO混合优化算法的软基水闸底板脱空反演

软基水闸底板脱空是水闸在长期服役期间受水流侵蚀等环境因素影响所产生的一种危害极大且难以察觉的病害。由于其病害部位于水下,传统方法难以检测,该研究提出一种基于高斯过程回归(Gaussian process regression,GPR)代理模型和遗传-自适应惯性权重粒子群(genetic algorithm-adaptive particle swarm optimization,GA-APSO)混合优化算法的水闸底板脱空动力学反演方法,用于检测软基水闸底板脱空。首先,构建表征软基水闸底板脱空参数和水闸结构模态参数之间非线性关系的GPR代理模型;其次,基于GPR代理模型与水闸实测模态参数建立脱空反演的最优化数学模型,将反演问题转化为目标函数最优化求解问题;最后,为提高算法寻优计算的精度,提出一种GA-APSO混合优化算法对目标函数进行脱空反演计算,并提出一种更合理判断反演脱空区域面积和实际脱空区域面积相对误差的指标—面积不重合度。为验证所提方法性能,以一室内软基水闸物理模型为例,对两种不同脱空工况开展研究分析,结果表明,反演脱空区域面积和模型实际设置脱空区域面积的相对误差分别为8.47%和10.77%,相对误差值较小,证明所提方法能有效反演出水闸底板脱空情况,可成为软基水闸底板脱空反演检测的一种新方法。

基于PSO–GPR含缺陷管道失效应力预测

传统对含缺陷管道失效应力的预测方法存在误差偏大的问题。针对该问题,利用MATLAB软件建立基于PSO–GPR(particle swarm optimization–Gaussian process regression)含缺陷管道失效应力预测模型。通过对60组含缺陷管道的试验数据进行测试,发现预测结果与实测结果均在95%置信区间内,表明可以将均值作为预测结果。对预测结果进行分析表明:高斯过程回归的预测结果与实测结果的最小相对误差为0.003%,最大相对误差为1.205%,平均相对误差为0.319%,基于预测结果和实测结果的散点均落在曲线y=x的±1.3%误差带中,验证了高斯过程回归预测模型的准确性,为管道的工程实际应用与维修提供较为精准的判断依据。

烟叶质量均质性指标构建与应用

烟叶质量均质性问题越来越受到烟草业界的关注。为了对烟叶质量均质性进行科学计量,本文利用变异系数和倒S型函数模型,提出了均质性指数(homogeneity,H)的概念,建立了计算模型,比较了逐步回归、支持向量机、高斯过程回归3种算法的预测精度,并用实例分析了空间维度和时间维度H值的变化特征。结果表明,高斯过程回归建立的模型最好,其均方根误差为0.18,决定系数为0.999 9,当CV值为0.5%~80.0%时,相对误差最高的仅为3.3%,绝大部分情况下低于0.3%。在空间维度上,烟叶化学成分H值在自治州、县、烟站尺度分别为73.3、71.8、89.5。一般情况下,空间范围越广,H值越小、均质性越差。在时间维度上,烟叶化学成分年度间稳定。

基于高斯过程回归预测的舞台机器人避撞路径滚动时域规划

针对移动舞台机器人的避撞运动规划问题,提出基于高斯过程回归和模型预测控制(MPC)的方法。在获取舞台环境中运动体位置信息后,使用高斯过程回归方法对运动体位置进行预测,再利用运动体的未来位置信息,将控制时域内存在的碰撞风险作为硬性约束,在线优化求解移动机器人未来一段时刻的局部最优速度轨迹。进一步驱动移动舞台机器人跟踪规划速度,实现对移动舞台机器人运动轨迹的实时滚动规划与跟踪。最后在两类舞台场景下,仿真验证了本文方法的有效性。

基于高斯过程回归矿山爆破飞石距离预测模型

为了提高矿山爆破飞石距离预测结果的精度,首先,根据马来西亚某矿山52次爆破作业记录的飞石距离相关参数,建立由6个输入(炮孔孔径、炮孔长度、最小抵抗线/孔距、炮孔填塞长度、最大一段装药量、炸药单耗)和1个输出(飞石距离)组成的数据库。然后,基于高斯过程回归机器学习算法,建立爆破飞石距离的预测模型,将其应用于马来西亚某矿山中,并与2种主流的机器学习方法(支持向量回归和神经网络)的预测结果进行对比。结果表明:从实际图-预测值图和残差分析看,基于双层神经网络构建的飞石距离预测模型的预测效果最差;从回归评价指标看,基于二次有理高斯过程回归建立的飞石距离预测模型的预测效果最优,其R-平方(R^(2))值为0.9、均方根误差(RMSE)值为24.67、均方误差(MSE)值为608.61、平均绝对误差(MAE)值为21.42。由此可知,基于高斯过程回归预测矿山爆破飞石距离更精确。可为类似矿山爆破安全警戒范围计算提供理论基础。

基于飞行数据的大气湍流参数化建模

面向特定湍流扰动风场建立参数化的湍流模型,对于研究大气扰动影响下的飞行动力学和飞行安全问题具有重要意义。基于民航飞机飞行数据,提出了大气湍流参数化建模方法。首先,基于原始飞行数据推算出沿航迹的三轴扰动风分量,并采用高斯过程回归分离出扰动风中的非湍流分量,经统计检验提取出符合平稳性和正态性的湍流成分;其次,基于极大似然估计建立湍流频域模型,基于自回归过程建立湍流时域模型。实际飞行数据的试验表明,所建立的参数化湍流模型能够实现湍流风场的定制化建模,可以进一步应用于飞行安全分析和飞行参数估计。

基于GGRA-GPR模型的洪涝灾害直接经济损失预评估

随着经济发展及洪涝灾害频率和强度的增加,灾后应急管理需快速了解灾害损失,需先从致灾因子、承灾体、孕灾环境、应急能力、灾情等5个方面构建指标体系,并基于广义灰色关联分析验证其合理性,其次引入高斯过程回归模型对洪涝灾害经济损失进行预评估模拟,最后运用该方法评估了京津冀城市群2010~2020年洪涝灾害直接经济损失。结果表明,对比单纯高斯过程回归与神经网络评估模型,广义灰色关联分析—高斯过程回归模型具有最优的拟合精度。