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A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm

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This study introduces a genetic algorithm radial basis neural network for improved Gross Domestic Product (GDP) forecasting. This method enhances macrocontrol planning by accurately predicting economic trends.

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Area of Science:

  • Economics
  • Computer Science
  • Artificial Intelligence

Background:

  • Gross Domestic Product (GDP) is a key indicator of economic status and development.
  • GDP is closely linked to inflation, unemployment, and economic growth rates, reflecting future economic trends.
  • Accurate GDP forecasting is crucial for effective government macrocontrol planning.

Purpose of the Study:

  • To investigate the application of a genetic algorithm radial basis neural network (GA-RBFNN) for GDP forecasting.
  • To evaluate the model's ability to capture complex relationships in historical economic data.
  • To compare the prediction accuracy and generalization ability of the GA-RBFNN model.

Main Methods:

  • Utilizing a genetic algorithm to optimize the center and smoothing factors of a radial basis function neural network.
  • Employing a stochastic learning method for efficient network training.
  • Modeling historical GDP data, potentially containing linear and nonlinear relationships.

Main Results:

  • The GA-RBFNN model demonstrates applicability in forecasting GDP.
  • The study validates the model's effectiveness in analyzing relationships within economic data sequences.
  • The model's prediction effect and generalization ability were compared and analyzed.

Conclusions:

  • The genetic algorithm radial basis neural network is a viable tool for GDP forecasting.
  • This approach aids in making timely and effective macrocontrol plans based on predicted economic trends.
  • The stochastic learning method offers an efficient way to train RBFNNs for economic analysis.