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.

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.

Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.

基于SSA-GPR模型的风电机组运行状态监测

为提高风电机组发电效率,增加经济收益,实现风电机组运行状态的在线监测,提出一种基于麻雀搜索算法优化高斯过程回归(SSA-GPR)模型的风电机组状态监测新方法。首先对数据采集与监视控制(SCADA)系统采集到的数据进行预处理分析,利用相关性分析完成模型的输入量选择;然后利用机组正常运行状态下的参数建立常态回归模型,实时计算重构误差,通过实时监测功率残差值是否超过动态故障阈值来判断机组状态。实例结果表明,所提方法的预测误差更小,并可以提前120 min实现机组异常运行状态预警。

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。

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.

基于VMD和DAIPSO-GPR解决容量再生现象的锂离子电池寿命预测研究

锂离子电池应用时表现出的时变、动态、非线性等特征,以及容量再生现象,导致传统模型对锂离子电池剩余使用寿命(RUL)预测的准确性低,该文将变分模态分解(VMD)和高斯过程回归(GPR)以及动态自适应免疫粒子群(DAIPSO)结合,建立RUL预测模型。首先利用等压降放电时间分析法,提取健康因子,利用VMD对其进行分解处理,挖掘数据内在信息,降低数据复杂度,并针对不同分量,利用不同协方差函数建立GPR预测模型,有效捕获了数据的长期下降趋势和短期再生波动。利用DAIPSO算法优化GPR模型,实现核函数超参数的优化,建立了更准确的退化关系模型,最终实现剩余使用寿命的准确预测,以及不确定性表征。最后利用NASA电池数据进行验证,离线预测结果表明所提方法具有较高预测精度和泛化适应能力。

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。

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。

一种动态校正的AGMM-GPR多模型软测量建模方法

工业过程常常是强非线性的,并有多个工况,传统的软测量方法存在预测能力差,不能有效利用误差信息等缺点.为了有效解决这些问题,提出一种基于自适应高斯混合模型-高斯过程回归(AGMM-GPR)的多模型动态校正软测量建模方法.首先,通过贝叶斯信息准则构建自适应高斯混合模型(AGMM),得到优化的子模型个数;然后,利用GPR方法建立各局部模型,当新的数据到来时,将其隶属于各局部模型的后验概率和预测值融合得到多模型输出;最后,为了进一步提高模型的精度,构建自回归积分滑动平均(ARIMA)模型对多模型输出进行动态反馈校正.通过数值仿真和硫回收装置(SRU)中H2S浓度的估计,验证了所提方法具有良好的预测精度和泛化性能.

鱼群算法优化组合核函数GPR的油井动液面预测

针对抽油井动液面(DFL)检测主要依靠人工操作回声仪测试,无法实时在线检测,而单一核函数的高斯过程回归(GPR)无法明显提高预测精度和泛化能力,提出了一种人工鱼群算法(AFSA)优化组合核函数的动态高斯过程回归动液面预测模型.采用多项式函数、线性函数与径向基函数组合构建核函数,利用人工鱼群算法对核函数模型参数进行寻优,采用快速傅里叶变换(FFT)和核主元分析(KPCA)融合提取时频数据非线性特征作为模型输入,提高模型的预测精度和泛化能力.油田现场应用验证了该方法的有效性.

基于GSO-GPR算法的岩层移动角预测模型及其应用

为快速准确获取金属矿地下开采岩层移动角,提出基于群搜索优化(GSO)算法的改进高斯过程回归(GPR)理论。以矿体上下盘围岩性质、上下盘围岩稳固程度、地下水情况、矿体走向长度、矿体倾角、开采厚度及深度9个影响因素作为判别指标,结合35组实测数据建立金属矿充填开采岩层移动角学习预测模型。将该模型应用于三山岛金矿岩层移动角预测,利用UDEC数值模拟结果进行对比验证,同时分析三山岛金矿海下开采对上盘竖井的影响。研究结果表明:(1)GSO-GPR预测模型对地下开采岩层移动角的预测效果良好,预测精度在5%以内;(2)三山岛金矿上、下盘岩层移动角分别为72°和68°;(3)当前矿山开采未对竖井造成影响,但随着开采深入至-658 m水平,竖井将进入塌陷范围。

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。

车辆自适应巡航下的MPC方法的研究

提出了一种新颖的车辆自适应巡航控制(ACC)系统,该系统可以在保障跟车距离的同时,提升车辆的燃油经济性。基于模型预测控制(MPC)进行ACC的上层控制器搭建,并在此基础上采用高斯过程回归(GPR),网格搜索(GS)和自适应方法进行三轨参数调整,将控制域和预测域调整到最优状态。此外,为了减少计算量并提高稳定性,系统采用了粒子群优化算法(PSO)对系统进行升级改进。仿真结果表明,基于MPC控制的车辆自适应巡航控制系统可以在保证良好跟踪性能的同时降低燃油消耗率。

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.

基于GPR-DE模型的CO_(2)-原油体系最小混相压力研究

在对中外35个CO_(2)驱油藏注入气体组分、油藏温度、原油组分、注入气体临界温度和最小混相压力进行数据统计和处理的基础上,结合高斯过程回归(GPR)与差分进化算法(DE),建立了预测CO_(2)-原油体系最小混相压力的新模型——GPR-DE模型。利用统计误差和图形误差评价GPR-DE模型的精确度,利用实验数据和敏感性分析对模型结果进行了验证,并与现有模型的预测结果进行对比。结果表明,GPR-DE模型与其他模型相比,精确度更高、应用范围更广,平均绝对相对误差仅为2.060%,标准差仅为0.0532。GPR-DE模型不仅可以预测CO_(2)-原油体系最小混相压力,还可以预测其他气体与原油体系最小混相压力。

GPR、XGBoost和CatBoost模拟江西地区参考作物蒸散量的适应性研究

【目的】提高机器学习模型模拟参考作物蒸散量在江西省适应性和精度。【方法】基于江西南昌等15个气象站2001—2015年日值气象数据(最高气温、最低气温、地表辐射、大气顶层辐射、相对湿度和2 m高风速),以FAO-56Penman-Monteith(P-M)公式的计算结果作为对照,建立了计算ET0的高斯过程回归(GPR)、极限梯度提升(XGBoost)和梯度提升决策树(CatBoost)模型,并分别与经验模型进行比较。【结果】各气象参数对机器学习模型模拟ET0的精度影响由大到小依次为:Rs、Tmax和Tmin、RH、U2,且采用Tmax、Tmin、Rs和RH气象参数组合的机器学习模型(RMSE<0.2mm/d)模拟ET0精度高。此外,3种机器学习模型在有限的气象数据时具有较好的适用性,且优于传统经验模型,其中GPR和CatBoost模型的预测精度高,但GPR模型稳定性最好。【结论】考虑到所研究模型调参的复杂性、预测精度和稳定性,GPR模型可作为江西地区参考作物蒸散量模拟的推荐方法。