期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到100篇文章
< 1 2 … 5 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
1
作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
暂未订购 下载PDF
Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications 认领 引用
2
作者 Selvaraj Vedhanayaki Vairavasundaram Indragandhi 《Global Energy Interconnection》 EI CSCD 2025年第5期891-904,共14页
The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a... The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R2,root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately. 展开更多
关键词 Lithium-ion batteries State of health Machine learning algorithms Bayesian optimization Kernel function
暂未订购 下载PDF
A Primal-dual Large-update Interior-point Algorithm for P_*(κ)-LCP Based on a New Class of Kernel Functions 认领 引用
3
作者 Ping JI Ming-wang ZHANG Xin LI 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2018年第1期119-134,共16页
In this paper, we propose a large-update primal-dual interior point algorithm for P_＊（κ）-linear complementarity problem. The method is based on a new class of kernel functions which is neither classical logarithmi... In this paper, we propose a large-update primal-dual interior point algorithm for P_＊（κ）-linear complementarity problem. The method is based on a new class of kernel functions which is neither classical logarithmic function nor self-regular functions. It is determines both search directions and the proximity measure between the iterate and the center path. We show that if a strictly feasible starting point is available, then the new algorithm has O（1 ＋ 2κ）p√n（1/plog n ＋ 1）^2 lognε iteration complexity which becomes O（（1 ＋ 2κ）√nlog n logn/ε）with special choice of the parameter p. It is matches the currently best known iteration bound for P＊（κ）-linear complementarity problem. Some computational results have been provided. 展开更多
关键词 interior-point method kernel function complexity linear complementarity problem
暂未订购 下载PDF
Numerical simulation of the fluid and flexible rods interaction using a semi-resolved coupling model promoted by anisotropic Gaussian kernel function 认领 引用
4
作者 Caiping Jin Jingxin Zhang Yonglin Sun 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2025年第1期5-8,共4页
The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computatio... The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computational fluid dynamics and the flexible rod dynamics is proposed using a two-way domain expansion method.The gov-erning equations of the flexible rod dynamics are discretized and solved by the finite element method,and the fluid flow is simulated by the finite volume method.The interaction between fluids and solid rods is modeled by introducing body force terms into the momentum equations.Referred to the traditional semi-resolved numerical model,an anisotropic Gaussian kernel function method is proposed to specify the interactive forces between flu-ids and solid bodies for non-circle rod cross-sections.A benchmark of the flow passing around a single flexible plate with a rectangular cross-section is used to validate the algorithm.Focused on the engineering applications,a test case of a finite patch of cylinders is implemented to validate the accuracy and efficiency of the coupled model. 展开更多
关键词 Semi-resolved coupling model Two-way domain expansion method Anisotropic Gaussian kernel function Flexible rod(s)
暂未订购 下载PDF
SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control 认领 引用 被引量：17
5
作者 钟伟民 何国龙 +1 位作者 皮道映 孙优贤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2005年第3期373-379,共7页
A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identifica... A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection. 展开更多
关键词 nonlinear model predictive control support vector machine nonlinear system identification kernel function
暂未订购 下载PDF
Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel 认领 引用 被引量：25
6
作者 包哲静 皮道映 孙优贤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第5期691-697,共7页
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a... Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm. 展开更多
关键词 nonlinear model predictive control support vector machine with multi-kernel nonlinear system identification kernel function
暂未订购 下载PDF
Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics 认领 引用 被引量：6
7
作者 XU Dong-wei WANG Yong-dong +2 位作者 JIA Li-min ZHANG Gui-jun GUO Hai-feng 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第9期2453-2464,共12页
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers.In this work,an algorithm based on kernel-k nearest neighbor(KNN)matching of road traffic spatial character... The accurate estimation of road traffic states can provide decision making for travelers and traffic managers.In this work,an algorithm based on kernel-k nearest neighbor(KNN)matching of road traffic spatial characteristics is presented to estimate road traffic states.Firstly,the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC).Secondly,the spatial road traffic state data sequence was selected and the kernel function was constructed,with which the spatial road traffic data sequence could be mapped into a high dimensional feature space.Thirdly,the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained.Finally,the road traffic states were estimated from weighted averages of the selected k road traffic states,which corresponded to the nearest Euclidean distances.Several typical links in Beijing were adopted for case studies.The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27%and 91.32%respectively,which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy. 展开更多
关键词 road traffic kernel function k nearest neighbor(KNN) state estimation spatial characteristics
暂未订购 下载PDF
Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model 认领 引用 被引量：8
8
作者 LI Li-min CHENG Shao-kang WEN Zong-zhou 《Journal of Mountain Science》 SCIE CSCD 2021年第8期2130-2142,共13页
Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a... Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response. 展开更多
关键词 Landslide probability LSSVR Mixed kernel function Improved PCA Warning level
暂未订购 下载PDF
An algorithm for moving target detection in IR image based on grayscale distribution and kernel function 认领 引用 被引量：7
9
作者 王鲁平 张路平 +1 位作者 赵明 李飚 《Journal of Central South University》 SCIE EI CAS 2014年第11期4270-4278,共9页
A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de... A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms. 展开更多
关键词 moving target detection gray-weighted kernel function dynamic background
暂未订购 下载PDF
A Full-Newton Step Feasible Interior-Point Algorithm for the Special Weighted Linear Complementarity Problems Based on a Kernel Function 认领 引用 被引量：2
10
作者 GENG Jie ZHANG Mingwang ZHU Dechun 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第1期29-37,共9页
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ... In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method. 展开更多
关键词 interior-point algorithm weighted linear complementarity problem full-Newton step kernel function iteration complexity
暂未订购 下载PDF
A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term 认领 引用 被引量：2
11
作者 GENG Jie ZHANG Mingwang PANG Jinjuan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2020年第6期501-509,共9页
In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determini... In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determining the search directions and for measuring the distance between the given iterate and theμ-center for the algorithm.By developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update method.In our knowledge,this result is the first instance of full-Newton step feasible interior-point method for SDO which involving the kernel function. 展开更多
关键词 semidefinite optimization interior-point algorithm kernel function iteration complexity
暂未订购 下载PDF
An iterative modified kernel based on training data 认领 引用 被引量：1
12
作者 周志祥 韩逢庆 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2009年第1期121-128,共8页
To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thu... To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm. 展开更多
关键词 support vector regression data-dependent kernel function iteration
暂未订购 下载PDF
Research on Chinese place name recognition based on kernel classifier 认领 引用
13
作者 宇缨 王晓龙 +1 位作者 刘秉权 王慧 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2007年第1期79-82,共4页
A SVMs (Support Vector Machines) based method to identify Chinese place names is presented. In our approach, place name candidate is located according to a rational forming assumption, then SVMs based identification s... A SVMs (Support Vector Machines) based method to identify Chinese place names is presented. In our approach, place name candidate is located according to a rational forming assumption, then SVMs based identification strategy is used to distinguish whether one candidate is true place name or not. Referring to linguistic knowledge, basic semanteme of a contextual word and frequency information of words inside place name candidate are selected as features in our methodology. So dimension in the feature space is reduced dramatically and processing procedure is performed more efficiently. Result of open testing on unregistered place names achieves F-measure 83.25 in 8.17 million words news based on this project. 展开更多
关键词 SVMs Chinese place name feature selection semanteme kernel function
暂未订购 下载PDF
A data-adaptive network design for the regional gravity field modelling using spherical radial basis functions 认领 引用
14
作者 Fang Zhang Huanling Liu Hanjiang Wen 《Geodesy and Geodynamics》 EI CSCD 2024年第6期627-634,共8页
A high-precision regional gravity field model is significant in various geodesy applications.In the field of modelling regional gravity fields,the spherical radial basis functions(SRBFs)approach has recently gained wi... A high-precision regional gravity field model is significant in various geodesy applications.In the field of modelling regional gravity fields,the spherical radial basis functions(SRBFs)approach has recently gained widespread attention,while the modelling precision is primarily influenced by the base function network.In this study,we propose a method for constructing a data-adaptive network of SRBFs using a modified Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)algorithm,and the performance of the algorithm is verified by the observed gravity data in the Auvergne area.Furthermore,the turning point method is used to optimize the bandwidth of the basis function spectrum,which satisfies the demand for both high-precision gravity field and quasi-geoid modelling simultaneously.Numerical experimental results indicate that our algorithm has an accuracy of about 1.58 mGal in constructing the gravity field model and about 0.03 m in the regional quasi-geoid model.Compared to the existing methods,the number of SRBFs used for modelling has been reduced by 15.8%,and the time cost to determine the centre positions of SRBFs has been saved by 12.5%.Hence,the modified HDBSCAN algorithm presented here is a suitable design method for constructing the SRBF data adaptive network. 展开更多
关键词 Regional gravity field modelling Spherical radial basis functions Poisson kernel function HDBSCAN clustering algorithm
暂未订购 下载PDF
Interior-point algorithm based on general kernel function for monotone linear complementarity problem 认领 引用
15
作者 刘勇 白延琴 《Journal of Shanghai University(English Edition)》 2009年第2期95-101,共7页
A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Un... A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP. 展开更多
关键词 monotone linear complementarity problem （MLCP） interior-point method kernel function polynomial complexity
暂未订购 下载PDF
ASYMPTOTIC NORMALITY OF KERNEL ESTIMATES OF A DENSITY FUNCTION UNDER ASSOCIATION DEPENDENCE 认领 引用
16
作者 林正炎 《Acta Mathematica Scientia》 SCIE 2003年第3期345-350,共6页
Let {Xn, n≥1} be a strictly stationary sequence of random variables, which are either associated or negatively associated, f(.) be their common density. In this paper, the author shows a central limit theorem for a k... Let {Xn, n≥1} be a strictly stationary sequence of random variables, which are either associated or negatively associated, f(.) be their common density. In this paper, the author shows a central limit theorem for a kernel estimate of f(.) under certain regular conditions. 展开更多
关键词 Associated random variables negatively associated random variables kernel estimate of a density function central limit theorem
暂未订购 下载PDF
Interior-Point Algorithm for Linear Optimization Based on a New Kernel Function 认领 引用 被引量：2
17
作者 CHEN Donghai ZHANG Mingwang LI Weihua 《Wuhan University Journal of Natural Sciences》 CAS 2012年第1期12-18,共7页
In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barr... In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O（nlog（n/c）） and O（√nlog（n/ε））. This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions. 展开更多
关键词 linear optimization interior-point algorithms pri- mal-dual methods kernel function polynomial complexity
暂未订购 下载PDF
Kernelized fourth quantification theory for mineral target prediction 认领 引用
18
作者 CHEN Yongliang LI Xuebin LIN Nan 《Global Geology》 2011年第4期265-278,共14页
This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantifica- tion theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal w... This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantifica- tion theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal with the problem of mineral prediction without defining a training area. In mineral target prediction, the pre-defined statistical cells, such as grid cells, can be implicitly transformed using kernel techniques from input space to a high-dimensional feature space, where the nonlinearly separable clusters in the input space are ex- pected to be linearly separable. Then, the transformed cells in the feature space are mapped by the fourth quan- tifieation theory onto a low-dimensional scaling space, where the sealed cells can be visually clustered according to their spatial locations. At the same time, those cells, which are far away from the cluster center of the majority of the sealed cells, are recognized as anomaly cells. Finally, whether the anomaly cells can serve as mineral potential target cells can be tested by spatially superimposing the known mineral occurrences onto the anomaly ceils. A case study shows that nearly all the known mineral occurrences spatially coincide with the anomaly cells with nearly the smallest scaled coordinates in one-dimensional sealing space. In the case study, the mineral target cells delineated by the new model are similar to those predicted by the well-known WofE model. 展开更多
关键词 kernel function feature space fourth quantification theory nonlinear transformation mineral target prediction
暂未订购 下载PDF
Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples 认领 引用
19
作者 Yang Yu Zeyu Xiong +1 位作者 Yueshan Xiong Weizi Li 《Computers, Materials & Continua》 SCIE EI 2019年第7期103-117,共15页
Logistic regression is often used to solve linear binary classification problems such as machine vision,speech recognition,and handwriting recognition.However,it usually fails to solve certain nonlinear multi-classifi... Logistic regression is often used to solve linear binary classification problems such as machine vision,speech recognition,and handwriting recognition.However,it usually fails to solve certain nonlinear multi-classification problem,such as problem with non-equilibrium samples.Many scholars have proposed some methods,such as neural network,least square support vector machine,AdaBoost meta-algorithm,etc.These methods essentially belong to machine learning categories.In this work,based on the probability theory and statistical principle,we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification.We have compared our approach with other methods using non-equilibrium samples,the results show that our approach guarantees sample integrity and achieves superior classification. 展开更多
关键词 Logistic regression multi-classification kernel function density estimation non-equilibrium
暂未订购 下载PDF
Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes 认领 引用
20
作者 王恒骞 耿君先 陈磊 《Journal of Donghua University(English Edition)》 CAS 2023年第1期27-33,共7页
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 ... 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. 展开更多
关键词 seasonal and trend decomposition using loess(STL) multi-output Gaussian process regression combined kernel function polyester esterification process
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 5 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈