A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directl...A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.展开更多
The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simu...The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simultaneously. Therefore, it is of great significance to accurately predict the demand for electricity consumption for the production planning of electricity and the normal operation of the society. In this paper, a hybrid model is constructed to predict the electricity consumption in China. The structural breaks test of monthly electricity consumption in China from January 2010 to December 2016 is carried out by using the structural breaks unit root test. Based on the existence of structura breaks, the electricity consumption data are decomposed into low-frequency and high-frequency components by wavelet model, and the separated low frequency signal and high frequency signal are predicted by autoregressive integrated moving average(ARIMA) and nonlinear autoregressive neural network(NAR), respectively. Therefore the wavelet-ARIMA-NAR hybrid model is constructed. In order to compare the effect of the hybrid model, the structural time series(STS) model is applied to predicting the electricity consumption. The results of prediction error test show that the hybrid model is more accurate for electricity consumption prediction.展开更多
In this paper, a new triangular decomposition algorithm is proposed for ordinary differential polynomial systems, which has triple exponential computational complexity. The key idea is to eliminate one algebraic varia...In this paper, a new triangular decomposition algorithm is proposed for ordinary differential polynomial systems, which has triple exponential computational complexity. The key idea is to eliminate one algebraic variable from a set of polynomials in one step using the theory of multivariate resultant. This seems to be the first differential triangular decomposition algorithm with elementary computation complexity.展开更多
Triangular decomposition with different properties has been used for various types of problem solving.In this paper,the concepts of pure chains and square-free pure triangular decomposition(SFPTD)of zero-dimensional p...Triangular decomposition with different properties has been used for various types of problem solving.In this paper,the concepts of pure chains and square-free pure triangular decomposition(SFPTD)of zero-dimensional polynomial systems are defined.Because of its good properties,SFPTD may be a key way to many problems related to zero-dimensional polynomial systems.Inspired by the work of Wang(2016)and of Dong and Mou(2019),the authors propose an algorithm for computing SFPTD based on Gr¨obner bases computation.The novelty of the algorithm is that the authors make use of saturated ideals and separant to ensure that the zero sets of any two pure chains are disjoint and every pure chain is square-free,respectively.On one hand,the authors prove the arithmetic complexity of the new algorithm can be single exponential in the square of the number of variables,which seems to be among the rare complexity analysis results for triangular-decomposition methods.On the other hand,the authors show experimentally that,on a large number of examples in the literature,the new algorithm is far more efficient than a popular triangular-decomposition method based on pseudodivision,and the methods based on SFPTD for real solution isolation and for computing radicals of zero-dimensional ideals are very efficient.展开更多
基金The National Natural Science Foundation of China (No.61374194)
文摘A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.
基金National Social Science Foundation of China(No.18AGL028)Social Science Foundation of the Higher Education Institutions of Jiangsu Province,China(No.2018SJZDI070)Social Science Foundations of the Jiangsu Province,China(Nos.16ZZB004,17ZTB005)
文摘The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simultaneously. Therefore, it is of great significance to accurately predict the demand for electricity consumption for the production planning of electricity and the normal operation of the society. In this paper, a hybrid model is constructed to predict the electricity consumption in China. The structural breaks test of monthly electricity consumption in China from January 2010 to December 2016 is carried out by using the structural breaks unit root test. Based on the existence of structura breaks, the electricity consumption data are decomposed into low-frequency and high-frequency components by wavelet model, and the separated low frequency signal and high frequency signal are predicted by autoregressive integrated moving average(ARIMA) and nonlinear autoregressive neural network(NAR), respectively. Therefore the wavelet-ARIMA-NAR hybrid model is constructed. In order to compare the effect of the hybrid model, the structural time series(STS) model is applied to predicting the electricity consumption. The results of prediction error test show that the hybrid model is more accurate for electricity consumption prediction.
基金supported by the National Natural Science Foundation of China under Grant No.60821002the National Key Basic Research Project of China
文摘In this paper, a new triangular decomposition algorithm is proposed for ordinary differential polynomial systems, which has triple exponential computational complexity. The key idea is to eliminate one algebraic variable from a set of polynomials in one step using the theory of multivariate resultant. This seems to be the first differential triangular decomposition algorithm with elementary computation complexity.
基金supported by National Key R&D Program of China under Grant No.2022YFA1005102the National Natural Science Foundation of China under Grant No.61732001。
文摘Triangular decomposition with different properties has been used for various types of problem solving.In this paper,the concepts of pure chains and square-free pure triangular decomposition(SFPTD)of zero-dimensional polynomial systems are defined.Because of its good properties,SFPTD may be a key way to many problems related to zero-dimensional polynomial systems.Inspired by the work of Wang(2016)and of Dong and Mou(2019),the authors propose an algorithm for computing SFPTD based on Gr¨obner bases computation.The novelty of the algorithm is that the authors make use of saturated ideals and separant to ensure that the zero sets of any two pure chains are disjoint and every pure chain is square-free,respectively.On one hand,the authors prove the arithmetic complexity of the new algorithm can be single exponential in the square of the number of variables,which seems to be among the rare complexity analysis results for triangular-decomposition methods.On the other hand,the authors show experimentally that,on a large number of examples in the literature,the new algorithm is far more efficient than a popular triangular-decomposition method based on pseudodivision,and the methods based on SFPTD for real solution isolation and for computing radicals of zero-dimensional ideals are very efficient.
