为有效预测城市交通运输碳排放量,在STIRPAT模型(Stochastic Impacts by Regression on Population,Affluence and Technology)的基础之上,建立了由遗传算法优化的支持向量机(GA-SVR)预测模型。选取人口总量、人均GDP、机动车保有量、...为有效预测城市交通运输碳排放量,在STIRPAT模型(Stochastic Impacts by Regression on Population,Affluence and Technology)的基础之上,建立了由遗传算法优化的支持向量机(GA-SVR)预测模型。选取人口总量、人均GDP、机动车保有量、旅客周转量、货物周转量、城镇化率和碳排放强度作为影响指标,采用北京市1995年到2019年期间的数据进行分析。结果表明:采用GA-SVR模型进行预测得到的数据和实际值之间有着良好的拟合回归效果,训练集及测试集的相关系数分别为0.98281和0.96242,所以该模型具有良好的推广与学习能力;预测2020—2023年北京市交通运输碳排放量增长趋势变缓,但总量在持续上升,说明城市依旧面临着碳排放压力。展开更多
The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based...The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.展开更多
This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial ...This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial navigation system （SRINS）. GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 （？）/h for the Z-axis drift of RLG. Compared with the ra-dial basis function-neural network （RBF-NN） model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.展开更多
文摘为有效预测城市交通运输碳排放量,在STIRPAT模型(Stochastic Impacts by Regression on Population,Affluence and Technology)的基础之上,建立了由遗传算法优化的支持向量机(GA-SVR)预测模型。选取人口总量、人均GDP、机动车保有量、旅客周转量、货物周转量、城镇化率和碳排放强度作为影响指标,采用北京市1995年到2019年期间的数据进行分析。结果表明:采用GA-SVR模型进行预测得到的数据和实际值之间有着良好的拟合回归效果,训练集及测试集的相关系数分别为0.98281和0.96242,所以该模型具有良好的推广与学习能力;预测2020—2023年北京市交通运输碳排放量增长趋势变缓,但总量在持续上升,说明城市依旧面临着碳排放压力。
基金the Key Project of National Natural Science Foundation of China(No.61134009)Program for Changjiang Scholars and Innovation Research Team in University from the Ministry of Education,China(No.IRT1220)+1 种基金Specialized Research Fund for Shanghai Leading Talents,Project of the Shanghai Committee of Science and Technology,China(No.13JC1407500)the Fundamental Research Funds for the Central Universities,China(No.2232012A3-04)
文摘The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.
文摘This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial navigation system （SRINS）. GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 （？）/h for the Z-axis drift of RLG. Compared with the ra-dial basis function-neural network （RBF-NN） model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.
