Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune...Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.展开更多
As fundamental economic units,county economies play a crucial role in China's national economic system.However,it remains unclear whether intellectual property rights policies at the county level can promote econo...As fundamental economic units,county economies play a crucial role in China's national economic system.However,it remains unclear whether intellectual property rights policies at the county level can promote economic growth.We used a quasi-natural experiment from the Intellectual Property Powering County Project("the Project")to measure their impact on county economic growth from 2009 to 2020,applying a multi-period diference-in-differences method.County economic growth indicators were measured using Enhanced Vegetation Index-calibrated nighttime light data from the Defense Meteorological Satellite Program and Visible Infrared Imaging Radiometer Suite.The findings demonstrated that the Project promoted economic growth significantly by incentivizing innovation,attracting high-tech and new-tech enterprises,and fostering brand creation.The Project also exhibited significant heterogeneity across districts and counties,along with spillover effects on economic growth in surrounding counties within approximately 80 kilometers.展开更多
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
基金support from the China National Social Science Foundation (No.23BJY216)the Major Project of the key research institutes of Chinese Ministry of Education (No.22JJD790051)+1 种基金the Key Project of Social Science Foundation of Fujian Province (No.FJ2022A011)the Fundamental Research Funds for the Central Universities (No.20720191070).
文摘As fundamental economic units,county economies play a crucial role in China's national economic system.However,it remains unclear whether intellectual property rights policies at the county level can promote economic growth.We used a quasi-natural experiment from the Intellectual Property Powering County Project("the Project")to measure their impact on county economic growth from 2009 to 2020,applying a multi-period diference-in-differences method.County economic growth indicators were measured using Enhanced Vegetation Index-calibrated nighttime light data from the Defense Meteorological Satellite Program and Visible Infrared Imaging Radiometer Suite.The findings demonstrated that the Project promoted economic growth significantly by incentivizing innovation,attracting high-tech and new-tech enterprises,and fostering brand creation.The Project also exhibited significant heterogeneity across districts and counties,along with spillover effects on economic growth in surrounding counties within approximately 80 kilometers.
