Using Elman Neural Network Model to Forecast and Analyze the Agricultural Economy

Abstract

The agricultural economy covers a wide range and has many influencing factors. There are often serious problems of complexity and diversity. The traditional agricultural economic forecasting methods often ignore the complexity and diversity, and it is difficult to accurately describe the development law of the agricultural economy. To improve the accuracy of agricultural economic time series forecasting under the condition of complexity and diversity, this paper proposes an agricultural economic forecasting method based on Elman neural network structure. Firstly, the data are screened and processed according to the time series of agricultural economic changes, and those factors that are more important to the agricultural economy are screened out from the collected public data. Secondly, this paper designs an efficient Elman neural network topology and sends the selected important data into the neural network for data learning and neural network parameter optimization, to achieve a more accurate agricultural economic forecasting model. Finally, a large number of experimental results show that the method based on the Elman neural network structure can overcome the shortcomings of traditional methods. It can avoid the interference of human subjective will, realize the comprehensive and accurate description of the changing laws of the agricultural economy with time, and promote the development of the agricultural economy.