Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

Mingxun Zhu,Zhigang Meng

doi:10.1155/2021/2000159

Abstract

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

Highlights

Gross domestic product (GDP) is the most important indicator to measure national economic development and a comprehensive reflection of economic operation
It is of great significance to analyze and predict the annual, medium, and long-term GDP and put forward the policy ideas of GDP and economic development. e provincial economy is an important part of the national economy and is relatively independent. erefore, many research literatures believe that the provincial economy is a relatively independent research object and an important research level of macroeconomics [1, 2]
E input and output data are normalized, and the neural network model is constructed. rough the analysis of RBFNN, this paper proposes to establish the forecasting model of the Shandong economic region GDP based on genetic algorithm (GA) optimization and verifies the optimized forecasting model by simulation. e trained neural network module is modeled by Simulink and simulated, and the fixed interference experiment is carried out on the simulation model

Summary

Introduction

Gross domestic product (GDP) is the most important indicator to measure national economic development and a comprehensive reflection of economic operation. E provincial economy is an important part of the national economy and is relatively independent. Erefore, many research literatures believe that the provincial economy is a relatively independent research object and an important research level of macroeconomics [1, 2]. E original extensive development consumes too many resources, which makes the existing environmental resources bear a high load. In such an economic environment, it is of great significance to predict the future GDP and optimize the industrial structure

Methods

Discussion

Conclusion