Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traf...Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.展开更多
In order to solve the electromagnetic problems on the large multi branch domains, the decomposition projective method(DPM) is generalized for multi subspaces in this paper. Furthermore multi parameters are designed fo...In order to solve the electromagnetic problems on the large multi branch domains, the decomposition projective method(DPM) is generalized for multi subspaces in this paper. Furthermore multi parameters are designed for DPM, which is called the fast DPM(FDPM), and the convergence ratio of the above algorithm is greatly increased. The examples show that the iterative number of the FDPM with optimal parameters decreases much more, which is less than one third of the DPM iteration number. After studying the ...展开更多
基金The National Natural Science Foundation of China(No.71101014,50679008)Specialized Research Fund for the Doctoral Program of Higher Education(No.200801411105)the Science and Technology Project of the Department of Communications of Henan Province(No.2010D107-4)
文摘Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.
文摘In order to solve the electromagnetic problems on the large multi branch domains, the decomposition projective method(DPM) is generalized for multi subspaces in this paper. Furthermore multi parameters are designed for DPM, which is called the fast DPM(FDPM), and the convergence ratio of the above algorithm is greatly increased. The examples show that the iterative number of the FDPM with optimal parameters decreases much more, which is less than one third of the DPM iteration number. After studying the ...
