Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

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.

Quality prediction of batch process using the global-local discriminant analysis based Gaussian process regression model

The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.

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.

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression

Lithium-ion batteries(LIBs)are widely used in transportation,energy storage,and other fields.The prediction of the remaining useful life(RUL)of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery.In order to improve the prediction accuracy of the RUL of LIBs,a two-phase RUL early prediction method combining neural network and Gaussian process regression(GPR)is proposed.In the initial phase,the features related to the capacity degradation of LIBs are utilized to train the neural network model,which is used to predict the initial cycle lifetime of 124 LIBs.The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space.The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated,and the shortest distance is considered to have a similar degradation pattern,which is used to determine the initial Dual Exponential Model(DEM).In the second phase,GPR uses the DEM as the initial parameter to predict each test set’s early RUL(ERUL).By testing four batteries under different working conditions,the RMSE of all capacity estimation is less than 1.2%,and the accuracy percentage(AP)of remaining life prediction is more than 98%.Experiments show that the method does not need human intervention and has high prediction accuracy.

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)).

基于学习的无人驾驶车辆模型预测路径跟踪控制研究

针对无人驾驶车辆路径跟踪控制问题中预测模型准确性与计算成本平衡制约问题,本文提出了一种基于学习的模型预测(learning-based model predictive control, LB-MPC)路径跟踪控制策略。建立了车辆2自由度单轨动力学模型,深入分析了其与IPG TruckMaker模型单步响应误差随车速、踏板开度及前轮转向角的变化规律,设计了误差数据集构建和滚动更新方法,基于高斯过程回归(Gaussian process regression, GPR)建立了误差拟合模型,对标称单轨模型进行实时误差补偿修正。将误差修正模型作为预测模型,设计了路径跟踪代价函数,构建了二次规划优化问题,提出了一种基于学习的模型预测路径跟踪控制架构。基于IPG TruckMaker&Simulink联合仿真平台与实车平台,验证了所提GPR模型误差修正与LB-MPC路径跟踪控制策略的实时性与有效性。结果表明,相较于传统模型预测(model predictive control, MPC)路径跟踪控制策略,所提LB-MPC策略路径跟踪平均误差降低了23.64%。

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。

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。

基于Tri-training GPR的半监督软测量建模方法

集成学习因通过构建并结合多个学习器,常获得比单一学习器显著优越的泛化能力。但是在标记数据比例较少时,建立高性能的集成学习软测量模型依然是个挑战。针对这一个问题,提出一种基于半监督集成学习的软测量建模方法——Tri-training GPR模型。该建模策略充分发挥了半监督学习的优势,减轻建模过程对标记样本数据的需求,在低数据标签率下,仍能通过对无标记数据进行筛选从而扩充可用于建模的有标记样本数据集,并进一步结合半监督学习和集成学习的优势,提出一种新的选择高置信度样本的思路。将所提方法应用于青霉素发酵和脱丁烷塔过程,建立青霉素和丁烷浓度预测软测量模型,与传统的建模方法相比获得了更优的预测结果,验证了模型的有效性。

基于高斯过程回归模型对一回路泄漏率的预测

工况的剧烈变化可能导致一回路系统中法兰连接部位、泵的密封面等处发生泄漏。针对准确的泄漏物理模型很难建立的实际情况,在对不可测的温度应力参数进行正态随机游走的基础上,以高斯过程回归模型为替代模型对一回路泄露率进行预测,并对替代模型的不确定性进行定量分析。结果表明,高斯过程回归模型能够方便地实现对替代模型的不确定性评估,并且在小样本容量的情况下,能够实现对一回路泄漏率较准确的预测。

无人自主系统能力边界参数自适应判别方法

为有效应对仿真测试面临的维度灾难问题,降低传统全参数空间遍历中所需的仿真次数,需要获取针对性的仿真数据以准确反映实验数据建模特征,以较少的仿真次数获得信息量丰富且代表原始数据特征的样本。提出一种面向无人自主系统能力边界参数自适应判别的数字化仿真测试模型,采用多权重结构的佳点集进行初始构建,结合自适应核函数边界点判别算法,通过高斯过程回归对模型进行迭代优化,自适应地判别无人自主系统的能力边界。实验结果表明:该方法能够降低建模所需数据量,提高自适应参数边界判别的效率,为提升智能无人系统试验的效率提供了高效途径。

基于GPR模型的多孔沥青混合料空隙率预估

多孔沥青混合料空隙率是影响其排水功能和路用性能的关键指标之一。为实现多孔沥青混合料空隙率的快速判别,该研究以混合料级配不同筛孔尺寸通过率、油石比为自变量,通过相关性分析提取特征参数,进而基于高斯过程回归(GPR)模型建立PAC-13多孔沥青混合料空隙率预估模型,并对比分析GPR模型与多元线性回归、AdaBoost和随机森林法对多孔沥青混合料空隙率的预估准确性。结果表明,以4.75、2.36、1.18、0.6、0.3、0.15和0.075 mm的筛孔通过率,以及油石比作为模型参数的多孔沥青混合料空隙率GPR预估模型具有较好的准确性,线性拟合系数达到0.95;相比多元线性回归、AdaBoost和随机森林法,GPR模型对于多孔沥青混合料空隙率预估的适用性相对更优。

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。

基于刚度模型和高斯过程回归模型的重载工业机器人分步标定方法

针对串联工业机器人由于关节柔性导致的重载下绝对定位精度较低的问题,提出了一种机器人定位误差分步标定方法。采用局部指数积模型对机器人进行几何误差标定。提出了一种基于建模和机器学习的非几何误差标定方法。在该部分中,首先建立了机器人的刚度模型对非几何误差中最主要的变形误差进行标定,然后采用数据驱动的高斯过程回归(GPR)模型对残余误差进行标定。实验结果表明,该方法可以有效提高机器人带载下的绝对定位精度,并且具有位置精度不随载荷变化而产生明显波动的优点。

绿色高性能混凝土最佳配合比研究

聚焦绿色高性能混凝土(Green High Performance Concrete,GHPC)的最佳配合比,旨在实现保持卓越性能的同时最大限度地利用工业废渣,如粉煤灰、超细矿渣、硅灰等。通过深入研究最佳配合比,能够精确调控GHPC的工作性能、强度和耐久性,以满足可持续建筑的标准。研究采用了贝叶斯算法优化后的高斯过程回归模型,通过125组试验结果进行训练,最终确定了FL 11%、SF 0%、SL 16%的最佳混合比。通过对比预测与试验结果,验证了模型的可靠性,误差控制在2%以内。该研究为GHPC的配合比提供了科学依据,为推动环保建筑材料的可持续发展提供了实用的指导。

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.