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Coal Requirement Prediction Using BP Neural Network

Authors: Xu Xin; Xuli Hong;

Coal Requirement Prediction Using BP Neural Network

Abstract

Coal is one of the most important main energy-consuming resources in our society. It is important to forecast the coal requirement with high accuracy. BP neural network forecasting model has the typical of self-learning and self-adaptation. It is often used in these systems that are difficult to create accurate mathematical model. The factors such as the trend of the industrial coal, the rate of increased GDP, rice index and the proportion in the energy-consuming of coal are considered in this paper. We use improved BP model to predict and simulate in MATLAB. It proves that this prediction has better application.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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