Application Of Neural Network In The Study Of The Relation And Development Trend Of Energy And GDP In China

In recent years,with the continuous innovation and development of learning algorithm and network structure of artificial neural network,artificial neural network has been widely used in many fields such as industrial production and life services.In the research of energy and economic development,artificial neural network has also been widely used.At present,the application of neural networks in the field of energy impact on economic development mainly focuses on the prediction of a certain index variable,and the types of artificial neural networks commonly used are relatively single.In order to analyze the relationship between energy and economic development more deeply,we use a variety of neural network models to study these related information in detail.The first part mainly discusses the neural network method suitable for energy and economic analysis.The main conclusions of the methodological research in this area are:(1)BP neural network and RBF network can not only predict variables,but also analyze the influence weight of input variables on target variables.This function can be realized by calculating network weights.(2)Through the analysis of input variables fitting to target variables,the evaluation and selection of input variables are realized.(3)Based on the prediction of multiple variables,the suggestions of using artificial neural network to predict variables are put forward.In the second part,we use a variety of artificial neural network methods to study the impact of energy on China’s GDP from the perspective of energy consumption and energy import.From the point of view of energy consumption,we use the method of BP neural network to fit the change trend of GDP through the consumption of all kinds of energy sources,and calculate the influence of each energy consumption on China’s GDP by the weight of the network.From the perspective of energy import,considering the large difference of energy import data,we construct a variable of energy import consumption ratio.Firstly,we use BP neural network to evaluate and screen the variable and the original variable.Then the radial basis function network is used to further analyze the variables.The influence on China’s GDP.In addition,the paper further investigates the influence of international energy market on China’s energy imports and the countermeasures of China,and discusses the current situation of energy consumption and energy structure in China,as well as the energy problems that restrict China’s GDP.The main conclusions of this section are as follows:(1)The proportion of clean energy in the energy structure has increased significantly However.coal still accounts for a huge proportion of energy consumption in China.(2)Crude oil import is an important factor restricting the development of China’s GDP.(3)Suggestions are made on the current energy problems in China.