In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditiona...In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University. The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students’ test data, and has good application value.展开更多
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
By using Fort theorem the generic stability result for the system of generalized vector equilibrium problems is established. Further, by proving the existence and connectivity of minimal essential set the existence re...By using Fort theorem the generic stability result for the system of generalized vector equilibrium problems is established. Further, by proving the existence and connectivity of minimal essential set the existence result of essential components in the solution set is derived.展开更多
Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Further...Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.展开更多
Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software pack...Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.展开更多
This article is concerned with the existence of global attractor of a weakly dissipative generalized two-component μ-Hunter-Saxton （gμHS2） system with viscous terms. Under the period boundary conditions and with t...This article is concerned with the existence of global attractor of a weakly dissipative generalized two-component μ-Hunter-Saxton （gμHS2） system with viscous terms. Under the period boundary conditions and with the help of the Galerkin procedure and compactness method, we first investigate the existence of global solution for the viscous weakly dissipative （gμHS2） system. On the basis of some uniformly prior estimates of the solution to the viscous weakly dissipative （gμHS2） system, we show that the semi-group of the solution operator {S（t）}t≥0 has a bounded absorbing set. Moreover, we prove that the dynamical system {S（t）}t≥0 possesses a global attractor in the Sobolev space H2（S） × H2（S）.展开更多
This paper analyses the synchronization problem of a generator onto power system without satisfying synchronization condition. The main focus of the paper is on the impact of the dc component of the current in the hig...This paper analyses the synchronization problem of a generator onto power system without satisfying synchronization condition. The main focus of the paper is on the impact of the dc component of the current in the high voltage circuit breaker during its close-open operating cycle. Using real time measurements of currents/voltages and angles during the close-opening cycle of high voltage generator circuit breaker and the impact of the dc component of current in context of interrupting large magnitude of current from the circuit breaker. In addition, the paper describes a study case model and the results of simulations performed using the software EMTP-ATP of an actual incident that occurred during the inadvertent synchronization of a large 339 MW, 24 kV generator to the grid.展开更多
When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model ...When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be ef...Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.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
We study the generalized Darboux transformation to the three-component coupled nonlinear Schr ¨odinger equation.First-and second-order localized waves are obtained by this technique.In first-order localized wave,...We study the generalized Darboux transformation to the three-component coupled nonlinear Schr ¨odinger equation.First-and second-order localized waves are obtained by this technique.In first-order localized wave,we get the interactional solutions between first-order rogue wave and one-dark,one-bright soliton respectively.Meanwhile,the interactional solutions between one-breather and first-order rogue wave are also given.In second-order localized wave,one-dark-one-bright soliton together with second-order rogue wave is presented in the first component,and two-bright soliton together with second-order rogue wave are gained respectively in the other two components.Besides,we observe second-order rogue wave together with one-breather in three components.Moreover,by increasing the absolute values of two free parameters,the nonlinear waves merge with each other distinctly.These results further reveal the interesting dynamic structures of localized waves in the three-component coupled system.展开更多
In the accelerator control system,many components use PI regulators for feedback control of the closed-loop system.In the process of parameter setting of PI regulators,empirical methods are often required and a lot of...In the accelerator control system,many components use PI regulators for feedback control of the closed-loop system.In the process of parameter setting of PI regulators,empirical methods are often required and a lot of time is spent on parameter setting.This paper proposes a serial algorithm for generating multiple dimensional variables,which covld greatly improve the speed of parameter setting,reduce the time of manual parameter setting,and provide a fast and feasible method for parameter setting of equipment in accelerators that requires manual intervention solution.展开更多
针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于...针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于改进型SOGI-FLL的单相并网逆变器电压控制方法。该方法在常规SOGI-FLL控制的基础上,在电压信号输入端加入级联型谐振滤波环节来消除谐波分量;同时引入直流控制环节,借助输入电压误差估计值来消除直流分量,达到电网电压频率和相位快速跟踪效果,从而实现电压的自适应控制。使用MATLAB及RT-LAB硬件在环半实物平台,在频率突变、含直流分量及谐波分量的非理想电网环境中,对二阶广义积分器锁相环、双二阶广义积分器锁频环与改进型SOGI-FLL 3种控制方法进行仿真及实验。结果表明,所提改进型SOGI-FLL控制方法在消除直流及谐波干扰的同时,能在0.025 s内实现频率锁定,且频率偏差小于2%,可增强系统对非理想电网信号的适应能力,实现并网电压的快速跟踪,具有良好动态性能。展开更多
文摘In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University. The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students’ test data, and has good application value.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
基金Supported by NSF of Chongqing and Science Foundations of Chongqing Jia1otong University
文摘By using Fort theorem the generic stability result for the system of generalized vector equilibrium problems is established. Further, by proving the existence and connectivity of minimal essential set the existence result of essential components in the solution set is derived.
基金supported by NSFC(11471260)the Foundation of Shannxi Education Committee(12JK0850)
文摘Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.
基金supported by the Yonsei University Research Fund of 2021(2021-22-0060).
文摘Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
基金partially supported by NNSF of China(11571126,11701198)China Postdoctoral Science Foundation funded project(2017M622397)
文摘This article is concerned with the existence of global attractor of a weakly dissipative generalized two-component μ-Hunter-Saxton （gμHS2） system with viscous terms. Under the period boundary conditions and with the help of the Galerkin procedure and compactness method, we first investigate the existence of global solution for the viscous weakly dissipative （gμHS2） system. On the basis of some uniformly prior estimates of the solution to the viscous weakly dissipative （gμHS2） system, we show that the semi-group of the solution operator {S（t）}t≥0 has a bounded absorbing set. Moreover, we prove that the dynamical system {S（t）}t≥0 possesses a global attractor in the Sobolev space H2（S） × H2（S）.
文摘This paper analyses the synchronization problem of a generator onto power system without satisfying synchronization condition. The main focus of the paper is on the impact of the dc component of the current in the high voltage circuit breaker during its close-open operating cycle. Using real time measurements of currents/voltages and angles during the close-opening cycle of high voltage generator circuit breaker and the impact of the dc component of current in context of interrupting large magnitude of current from the circuit breaker. In addition, the paper describes a study case model and the results of simulations performed using the software EMTP-ATP of an actual incident that occurred during the inadvertent synchronization of a large 339 MW, 24 kV generator to the grid.
文摘When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
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.
基金Project supported by the Global Change Research Program of China(Grant No.2015CB953904)the National Natural Science Foundation of China(Grant Nos.11275072 and 11435005)+2 种基金the Doctoral Program of Higher Education of China(Grant No.20120076110024)the Network Information Physics Calculation of Basic Research Innovation Research Group of China(Grant No.61321064)Shanghai Collaborative Innovation Center of Trustworthy Software for Internet of Things,China(Grant No.ZF1213)
文摘We study the generalized Darboux transformation to the three-component coupled nonlinear Schr ¨odinger equation.First-and second-order localized waves are obtained by this technique.In first-order localized wave,we get the interactional solutions between first-order rogue wave and one-dark,one-bright soliton respectively.Meanwhile,the interactional solutions between one-breather and first-order rogue wave are also given.In second-order localized wave,one-dark-one-bright soliton together with second-order rogue wave is presented in the first component,and two-bright soliton together with second-order rogue wave are gained respectively in the other two components.Besides,we observe second-order rogue wave together with one-breather in three components.Moreover,by increasing the absolute values of two free parameters,the nonlinear waves merge with each other distinctly.These results further reveal the interesting dynamic structures of localized waves in the three-component coupled system.
文摘In the accelerator control system,many components use PI regulators for feedback control of the closed-loop system.In the process of parameter setting of PI regulators,empirical methods are often required and a lot of time is spent on parameter setting.This paper proposes a serial algorithm for generating multiple dimensional variables,which covld greatly improve the speed of parameter setting,reduce the time of manual parameter setting,and provide a fast and feasible method for parameter setting of equipment in accelerators that requires manual intervention solution.
文摘针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于改进型SOGI-FLL的单相并网逆变器电压控制方法。该方法在常规SOGI-FLL控制的基础上,在电压信号输入端加入级联型谐振滤波环节来消除谐波分量;同时引入直流控制环节,借助输入电压误差估计值来消除直流分量,达到电网电压频率和相位快速跟踪效果,从而实现电压的自适应控制。使用MATLAB及RT-LAB硬件在环半实物平台,在频率突变、含直流分量及谐波分量的非理想电网环境中,对二阶广义积分器锁相环、双二阶广义积分器锁频环与改进型SOGI-FLL 3种控制方法进行仿真及实验。结果表明,所提改进型SOGI-FLL控制方法在消除直流及谐波干扰的同时,能在0.025 s内实现频率锁定,且频率偏差小于2%,可增强系统对非理想电网信号的适应能力,实现并网电压的快速跟踪,具有良好动态性能。
