针对自适应滤波领域的最小均方(Least Mean Square,LMS)算法无法权衡稳态误差和收敛速度这一矛盾,提出了一种改进的变步长LMS自适应滤波算法。该算法在基于对数函数的变步长LMS算法的基础上,建立了一种新的步长参数与误差的关系模型。...针对自适应滤波领域的最小均方(Least Mean Square,LMS)算法无法权衡稳态误差和收敛速度这一矛盾,提出了一种改进的变步长LMS自适应滤波算法。该算法在基于对数函数的变步长LMS算法的基础上,建立了一种新的步长参数与误差的关系模型。仿真结果表明,提出算法与已有算法相比,能够达到更高的收敛精度及更快的收敛速度,在系统不发生时变时,收敛精度分别提高了5 dB和3 dB,当系统发生时变后,收敛精度分别提高了4 dB和2 dB,不论系统是否发生时变,收敛速度都更快。展开更多
High-speed rail(HSR)has been an important driver of China s economic expansion over the last decade.Using data of 285 prefecture-level cities over 2010-2014,this paper proposes an endogenous economic growth model to e...High-speed rail(HSR)has been an important driver of China s economic expansion over the last decade.Using data of 285 prefecture-level cities over 2010-2014,this paper proposes an endogenous economic growth model to explain how and why HSR may have propelled China s economic growth by reducing the time-space between cities.The research results show that HSR has a potent effect on urban economic growth and regional convergence.Ceteris paribus,HSR appears to have accelerated economic growth by more than 0.6 percent and the pace of regional economic convergence by approximately 2 percent per annum over the data period.Our research findings have important policy implications for the sustainability of China s economic development,backed by HSR.展开更多
Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the mode...Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the model of combined GPS/BDS precise point positioning,and then analyzed the convergence speed and short-time(6 h)positioning accuracy.The calculation results show that in static positioning,the average convergence time of GPS is about 50 min,and its horizontal accuracy is better than 2 cm while the vertical accuracy is better than 4 cm.The convergence speed of combined GPS/BDS is about 40 min,and its positioning accuracy is close to that of GPS.In kinematic positioning,the average convergence time of GPS is about 72 min,and its horizontal accuracy is better than 5 cm while the vertical accuracy is better than 12 cm.The average convergence time of GPS/BDS is about 57 min,and its horizontal accuracy is better than 3 cm while the vertical accuracy is better than 9 cm.Combined GPS/BDS has significantly improved the convergence speed,and its positioning accuracy is slightly than that of GPS.展开更多
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ...In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.展开更多
基金the National Social Science Foundation of China(No.l8ZDA005)the National Natural Science Foundation of China(No.71673033)the Ministry of Education Social Science Foundation of China(No.16YJA790058).
文摘High-speed rail(HSR)has been an important driver of China s economic expansion over the last decade.Using data of 285 prefecture-level cities over 2010-2014,this paper proposes an endogenous economic growth model to explain how and why HSR may have propelled China s economic growth by reducing the time-space between cities.The research results show that HSR has a potent effect on urban economic growth and regional convergence.Ceteris paribus,HSR appears to have accelerated economic growth by more than 0.6 percent and the pace of regional economic convergence by approximately 2 percent per annum over the data period.Our research findings have important policy implications for the sustainability of China s economic development,backed by HSR.
基金supported by Director Foundation of the Institute of Seismology,China Earthquake Administration(6110).
文摘Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the model of combined GPS/BDS precise point positioning,and then analyzed the convergence speed and short-time(6 h)positioning accuracy.The calculation results show that in static positioning,the average convergence time of GPS is about 50 min,and its horizontal accuracy is better than 2 cm while the vertical accuracy is better than 4 cm.The convergence speed of combined GPS/BDS is about 40 min,and its positioning accuracy is close to that of GPS.In kinematic positioning,the average convergence time of GPS is about 72 min,and its horizontal accuracy is better than 5 cm while the vertical accuracy is better than 12 cm.The average convergence time of GPS/BDS is about 57 min,and its horizontal accuracy is better than 3 cm while the vertical accuracy is better than 9 cm.Combined GPS/BDS has significantly improved the convergence speed,and its positioning accuracy is slightly than that of GPS.
文摘In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.
