Let λ and μ are sequence spaces and have both the signed_weak gliding hump property, (λ,μ) be the algebra of the infinite matrix operators which transform λ into μ, in this paper, we study the strong? Mackey...Let λ and μ are sequence spaces and have both the signed_weak gliding hump property, (λ,μ) be the algebra of the infinite matrix operators which transform λ into μ, in this paper, we study the strong? Mackey? weak multiplier sequentially continuous problem of infinite matrix algebras (λ,μ).展开更多
Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the al...Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the algorithm has two shortcomings, the order must be even and the output might be a trivial factor. Actually, these drawbacks can be overcome if the number is an RSA modulus. Applying the special structure of the RSA modulus, an algorithm is presented to overcome the two shortcomings. The new algorithm improves Shor’s algorithm for factoring RSA modulus. The cost of the factorization algorithm almost depends on the calculation of the order of 2 in the multiplication group.展开更多
The main purpose of this paper is to introduce the general Smarandache mul- tiplicative sequence based on the Smarandache multiplicative sequence, and calculate the value of some infinite series involving these sequen...The main purpose of this paper is to introduce the general Smarandache mul- tiplicative sequence based on the Smarandache multiplicative sequence, and calculate the value of some infinite series involving these sequences.展开更多
This paper describes characteristics of (A p,α,l q) with 0【p≤1,α】0 and p≤q≤∞. As the corollaries, the characteristics of (A p,l q),(H p,l q),(G p,l q) and (B p,l q) with 0【p≤1 and p≤q≤∞ are ob...This paper describes characteristics of (A p,α,l q) with 0【p≤1,α】0 and p≤q≤∞. As the corollaries, the characteristics of (A p,l q),(H p,l q),(G p,l q) and (B p,l q) with 0【p≤1 and p≤q≤∞ are obtained. (H p,l q) and (H p,H q) with 1【p【∞ and 1≤q≤∞ are also studied.展开更多
In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio ...In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.展开更多
Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series...Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.展开更多
The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an app...The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.展开更多
This paper studies the autoregression models of order one, in a general time series setting that allows for weakly dependent innovations. Let {Xt} be a linear process defined by Xt =∑k=0^∞ψ kεt-k, where {ψk, k ≥...This paper studies the autoregression models of order one, in a general time series setting that allows for weakly dependent innovations. Let {Xt} be a linear process defined by Xt =∑k=0^∞ψ kεt-k, where {ψk, k ≥ 0} is a sequence of real numbers and {εk, k = 0, ±1, ±2,...} is a sequence of random variables. Two results are proved in this paper. In the first result, assuming that {εk, k ≥ 1} is a sequence of asymptotically linear negative quadrant dependent （ALNQD） random variables, the authors find the limiting distributions of the least squares estimator and the associated regression t statistic. It is interesting that the limiting distributions are similar to the one found in earlier work under the assumption of i.i.d, innovations. In the second result the authors prove that the least squares estimator is not a strong consistency estimator of the autoregressive parameter a when {εk, k ≥ 1} is a sequence of negatively associated （NA） random variables, and ψ0 = 1, ψk = 0, k ≥ 1.展开更多
Using the concept of base forces as state variables,a new finite element method-the base force element method (BFEM) on complementary energy principle for linear elasticity problems is presented.Firstly,an explicit ex...Using the concept of base forces as state variables,a new finite element method-the base force element method (BFEM) on complementary energy principle for linear elasticity problems is presented.Firstly,an explicit expression of compliance matrix for an element is derived through base forces by dyadic vectors.Then,the explicit control equations of finite element method of complementary energy principle are derived using Lagrange multiplier method.Thereafter,the base forces element procedure for linear elasticity is developed.Finally,several examples are analyzed to illustrate the reliability and accuracy of the formulation and the procedure.展开更多
Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory p...Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.展开更多
X-ray pulsars offer stable, periodic X-ray pulse sequences that can be used in spacecraft positioning systems. A method using X-ray pulsars to determine the initial orbit of a satellite is presented in this paper. Thi...X-ray pulsars offer stable, periodic X-ray pulse sequences that can be used in spacecraft positioning systems. A method using X-ray pulsars to determine the initial orbit of a satellite is presented in this paper. This method suggests only one detector to be equipped on the satellite and assumes that the detector observes three pulsars in turn. To improve the performance, the use of incremental phase in one observation duration is proposed, and the incremental phase is combined with the time difference of arrival(TDOA). Then, a weighted least squares(WLS) algorithm is formulated to calculate the initial orbit. Numerical simulations are performed to assess the proposed orbit determination method.展开更多
文摘Let λ and μ are sequence spaces and have both the signed_weak gliding hump property, (λ,μ) be the algebra of the infinite matrix operators which transform λ into μ, in this paper, we study the strong? Mackey? weak multiplier sequentially continuous problem of infinite matrix algebras (λ,μ).
文摘Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the algorithm has two shortcomings, the order must be even and the output might be a trivial factor. Actually, these drawbacks can be overcome if the number is an RSA modulus. Applying the special structure of the RSA modulus, an algorithm is presented to overcome the two shortcomings. The new algorithm improves Shor’s algorithm for factoring RSA modulus. The cost of the factorization algorithm almost depends on the calculation of the order of 2 in the multiplication group.
文摘The main purpose of this paper is to introduce the general Smarandache mul- tiplicative sequence based on the Smarandache multiplicative sequence, and calculate the value of some infinite series involving these sequences.
文摘This paper describes characteristics of (A p,α,l q) with 0【p≤1,α】0 and p≤q≤∞. As the corollaries, the characteristics of (A p,l q),(H p,l q),(G p,l q) and (B p,l q) with 0【p≤1 and p≤q≤∞ are obtained. (H p,l q) and (H p,H q) with 1【p【∞ and 1≤q≤∞ are also studied.
基金supported by the Science and Research projects for Ph.D. candidates in the faculty of Xuzhou Normal University (No.08XLR12)Natural Science Foundation of Xuzhou Normal University (No.09XLA10)
文摘In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.
文摘Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.
基金supported by National Natural Science Foundation of China(Grant Nos.11001124 and 91130007)the Doctoral Fund of Ministry of Eduction of China(Grant No.20110091110004)the General Research Fund from Hong Kong Research Grants Council(Grant No.HKBU 203712)
文摘The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.
基金supported by the National Natural Science Foundation of China under Grant Nos.10971081 and 11001104985 Project of Jilin University
文摘This paper studies the autoregression models of order one, in a general time series setting that allows for weakly dependent innovations. Let {Xt} be a linear process defined by Xt =∑k=0^∞ψ kεt-k, where {ψk, k ≥ 0} is a sequence of real numbers and {εk, k = 0, ±1, ±2,...} is a sequence of random variables. Two results are proved in this paper. In the first result, assuming that {εk, k ≥ 1} is a sequence of asymptotically linear negative quadrant dependent （ALNQD） random variables, the authors find the limiting distributions of the least squares estimator and the associated regression t statistic. It is interesting that the limiting distributions are similar to the one found in earlier work under the assumption of i.i.d, innovations. In the second result the authors prove that the least squares estimator is not a strong consistency estimator of the autoregressive parameter a when {εk, k ≥ 1} is a sequence of negatively associated （NA） random variables, and ψ0 = 1, ψk = 0, k ≥ 1.
基金supported by the National Natural Science Foundation of China (Grant No. 10972015)
文摘Using the concept of base forces as state variables,a new finite element method-the base force element method (BFEM) on complementary energy principle for linear elasticity problems is presented.Firstly,an explicit expression of compliance matrix for an element is derived through base forces by dyadic vectors.Then,the explicit control equations of finite element method of complementary energy principle are derived using Lagrange multiplier method.Thereafter,the base forces element procedure for linear elasticity is developed.Finally,several examples are analyzed to illustrate the reliability and accuracy of the formulation and the procedure.
基金supported by the National Natural Science Foundation of China under Grant Nos.71171011 and 91224001Program for New Century Excellent Talents in University(NCET-12-0756)
文摘Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.
基金supported by the National Natural Science Foundation of China(No.61401340)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2016JM6035)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.JB161303)and the Areospace T.T.&C.Innovation Program(No.201515A)
文摘X-ray pulsars offer stable, periodic X-ray pulse sequences that can be used in spacecraft positioning systems. A method using X-ray pulsars to determine the initial orbit of a satellite is presented in this paper. This method suggests only one detector to be equipped on the satellite and assumes that the detector observes three pulsars in turn. To improve the performance, the use of incremental phase in one observation duration is proposed, and the incremental phase is combined with the time difference of arrival(TDOA). Then, a weighted least squares(WLS) algorithm is formulated to calculate the initial orbit. Numerical simulations are performed to assess the proposed orbit determination method.
