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
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the ...This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.展开更多
With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued no...With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square(LMS)algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error(MSE)performance among of complex kernel LMS(KLMS)methods according to the specified kernel bandwidth and the length of dictionary.展开更多
Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover e...Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.展开更多
Predicting the power obtained at the output of the photovoltaic(PV)system is fundamental for the optimum use of the PV system.However,it varies at different times of the day depending on intermittent and nonlinear env...Predicting the power obtained at the output of the photovoltaic(PV)system is fundamental for the optimum use of the PV system.However,it varies at different times of the day depending on intermittent and nonlinear environmen-tal conditions including solar irradiation,temperature and the wind speed,Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve.In this study,a Gaussian kernel based Support Vector Regression(SVR)prediction model using multiple input variables is proposed for estimating the maximum power obtained from using per-turb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system.The performance of the kernel-based prediction model depends on the availability of a suitable ker-nel function that matches the learning objective,since an unsuitable kernel func-tion or hyper parameter tuning results in significantly poor performance.In this study for thefirst time in the literature both maximum power is obtained at max-imum power point and short-term maximum power estimation is made.While evaluating the performance of the suggested model,the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used.The maximum power obtained from the simu-lated system at maximum irradiance was 852.6 W.The accuracy and the perfor-mance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error(RMSE)and mean square error(MSE)values.MSE and RMSE rates which obtained were 4.5566*10-04 and 0.0213 using ANN model.MSE and RMSE rates which obtained were 13.0000*10-04 and 0.0362 using SWD-FFNN model.Using SVR model,1.1548*10-05 MSE and 0.0034 RMSE rates were obtained.In the short-term maximum power prediction,SVR gave higher prediction performance according to ANN and SWD-FFNN.展开更多
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
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上...提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。展开更多
In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance...In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.展开更多
基金supported by Shanghai 2021“Science and Technology Innovation Action Plan”:Social Development Science and Technology Research Project(Grant No.21DZ1202703).
文摘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.
基金Rahul Radhakrishnan received the B.Tech.degree in Applied Electronics and Instrumentation from the Government Engineering College,Calicut,India,in 2010 and the M.Tech.degreein Control Systems from the Department of Electrical Engineering,National Institute of Technology Kurukshetra,India,in 2013.He received the Ph.D.degree from the Department of Electrical Engineering,Indian Institute of Technology Patna,India,in 2018.Currently,he is workingasan Assistant Professor in the Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat,India.His main research interests include nonlinear filtering,aerospace,and underwater target tracking.
文摘This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.
基金supported by the National Natural Science Foundation of China(61001153,61271415,61401499,61531015)the Fundamental Research Funds for the Central Universities(3102014JCQ01010,3102014ZD0041)the Opening Research Foundation of State Key Laboratory of Underwater Information Processing and Control(9140C231002130C23085)
文摘With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square(LMS)algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error(MSE)performance among of complex kernel LMS(KLMS)methods according to the specified kernel bandwidth and the length of dictionary.
文摘Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.
文摘Predicting the power obtained at the output of the photovoltaic(PV)system is fundamental for the optimum use of the PV system.However,it varies at different times of the day depending on intermittent and nonlinear environmen-tal conditions including solar irradiation,temperature and the wind speed,Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve.In this study,a Gaussian kernel based Support Vector Regression(SVR)prediction model using multiple input variables is proposed for estimating the maximum power obtained from using per-turb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system.The performance of the kernel-based prediction model depends on the availability of a suitable ker-nel function that matches the learning objective,since an unsuitable kernel func-tion or hyper parameter tuning results in significantly poor performance.In this study for thefirst time in the literature both maximum power is obtained at max-imum power point and short-term maximum power estimation is made.While evaluating the performance of the suggested model,the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used.The maximum power obtained from the simu-lated system at maximum irradiance was 852.6 W.The accuracy and the perfor-mance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error(RMSE)and mean square error(MSE)values.MSE and RMSE rates which obtained were 4.5566*10-04 and 0.0213 using ANN model.MSE and RMSE rates which obtained were 13.0000*10-04 and 0.0362 using SWD-FFNN model.Using SVR model,1.1548*10-05 MSE and 0.0034 RMSE rates were obtained.In the short-term maximum power prediction,SVR gave higher prediction performance according to ANN and SWD-FFNN.
基金Natural Science Foundation of Shanghai,China(No.19ZR1402300)。
文摘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.
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
文摘提出基于知识度量的模糊粗糙c-均值聚类(fuzzy rough c-means based on the knowledge measure,KFRCM)算法。传统聚类算法在处理具有模糊边界的数据时存在一定的局限性,表现为对初始聚类中心较为敏感且在高维空间中效率较低。为解决上述问题,引入特征加权的知识度量,结合模糊隶属度函数与粗糙集近似算子,采用高斯核相似度以增强边界特性。实验采用14个数据集,实验结果表明,KFRCM算法的聚类准确性、稳定性和计算效率均优于6种主流聚类算法。该研究首次将知识度量与模糊粗糙聚类相结合,为开发更为可靠和适应性更强的聚类算法提供了新的思路和算法。
文摘In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.
