Intelligent forecasting of economic growth for African economies: Artificial neural networks versus time series and structural econometric models (original) (raw)
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International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/africa-economic-growth-forecasting-research-based-on-artificial-neural-network-model-case-study-of-benin https://www.ijert.org/research/africa-economic-growth-forecasting-research-based-on-artificial-neural-network-model-case-study-of-benin-IJERTV3IS111063.pdf Economic growth forecasting is important to make the policy on national economic development. Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real world sensor data, Artificial Neural Networks (ANN) are among the most effective learning methods currently know. The objective of this paper is to provide an intelligent algorithm and a practical in the design of a neural network for economic growth forecasting. Based on the original data from Benin economic database, we extracted the knowledge of economic classification, which includes attribute discretization, attribute importance ranking, attribute reduction and prediction rule. Then input the extracted key components into neural network as the input training sample. This method reduced the structure of neural network, and improved the training speed and the accuracy of prediction. The Benin Case study shows that the neural network can solve nonlinear problem and had been proved that the method is effective and feasible with high accuracy. The model has a good reference value for practical application.
Economic growth forecasting is important to make the policy on national economic development. Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real world sensor data, Artificial Neural Networks (ANN) are among the most effective learning methods currently know. The objective of this paper is to provide an intelligent algorithm and a practical in the design of a neural network for economic growth forecasting. Based on the original data from Benin economic database, we extracted the knowledge of economic classification, which includes attribute discretization, attribute importance ranking, attribute reduction and prediction rule. Then input the extracted key components into neural network as the input training sample. This method reduced the structure of neural network, and improved the training speed and the accuracy of prediction. The Benin Case study shows that the neural network can solve nonlinear problem and had been proved that the method is effective and feasible with high accuracy. The model has a good reference value for practical application.
International Journal of Economic and Environmental Geology, 2020
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