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Evaluating electric environmental issues using BP neural network with optimised hidden layer nodes

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Author Info
Dongxiao Niu
Jialiang Lv
Hongyan Liu
Abstract

BP neural network with optimum hidden layer nodes were used to evaluate the power environmental issues in China. Furthermore, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to optimise the number of hidden layer nodes of BP neural network. The evaluation indices were determined through the training of the BP neural network with the samples. This new method is validated through a case study where power environmental issues were evaluated, and high accuracy rate and simple computation complexity were achieved. Therefore, the number of hidden layer nodes can be determined more scientifically and evaluation results derived by our method tend to be more accurate.

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File URL: http://inderscience.metapress.com/link.asp?target=contribution&id=V670151138881802
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Publisher Info
Article provided by Inderscience Enterprises Ltd in its journal International Journal of Global Environmental Issues.

Volume (Year): 9 (2009)
Issue (Month): 3 (January)
Pages: 227-238
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:mes:ijgenv:v:9:y:2009:i:3:p:227-238

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Web page: http://inderscience.metapress.com/link.asp?target=journal&id=110856

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords: TOPSIS; back propagation; neural networks; hidden layer nodes; combination weight; environmental issues; electricity markets; power supply; China;

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This page was last updated on 2009-12-19.


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