In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B...In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.展开更多
A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to ...A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.展开更多
基金Project(50175110) supported by the National Natural Science Foundation of ChinaProject(2009bsxt019) supported by the Graduate Degree Thesis Innovation Foundation of Central South University, China
文摘In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.
基金Project(2014YJS080) supported by the Fundamental Research Funds for the Central Universities of China
文摘A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.
