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A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy

Gross domestic product (GDP) is an important indicator for determining a country’s or region’s economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country’s or regi...

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Main Authors: Qing, Zhang, Abdullah, Abdul Rashid, Choo, Wei Chong, Ali, Mass Hareeza
Format: Article
Published: Hindawi 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100443/
https://www.hindawi.com/journals/cin/2022/8235308/
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spelling my.upm.eprints.1004432023-12-01T08:32:19Z http://psasir.upm.edu.my/id/eprint/100443/ A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy Qing, Zhang Abdullah, Abdul Rashid Choo, Wei Chong Ali, Mass Hareeza Gross domestic product (GDP) is an important indicator for determining a country’s or region’s economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country’s or region’s future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence. Hindawi 2022-01 Article PeerReviewed Qing, Zhang and Abdullah, Abdul Rashid and Choo, Wei Chong and Ali, Mass Hareeza (2022) A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy. Computational Intelligence and Neuroscience, 2022. art. no. 8235308. pp. 1-12. ISSN 1687-5265; ESSN: 1687-5273 https://www.hindawi.com/journals/cin/2022/8235308/ 10.1155/2022/8235308
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Gross domestic product (GDP) is an important indicator for determining a country’s or region’s economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country’s or region’s future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.
format Article
author Qing, Zhang
Abdullah, Abdul Rashid
Choo, Wei Chong
Ali, Mass Hareeza
spellingShingle Qing, Zhang
Abdullah, Abdul Rashid
Choo, Wei Chong
Ali, Mass Hareeza
A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
author_facet Qing, Zhang
Abdullah, Abdul Rashid
Choo, Wei Chong
Ali, Mass Hareeza
author_sort Qing, Zhang
title A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
title_short A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
title_full A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
title_fullStr A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
title_full_unstemmed A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy
title_sort study on regional gdp forecasting analysis based on radial basis function neural network with genetic algorithm (rbfnn-ga) for shandong economy
publisher Hindawi
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100443/
https://www.hindawi.com/journals/cin/2022/8235308/
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score 13.211869

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