A Prediction Method of Power Energy Saving Potential Based on Rough Set Neural Network

Autor:	Ya Jun Wei, Jin Chao Li, Yu Zhi Zhao, Jin Ying Li
Rok vydání:	2010
Předmět:	Engineering Artificial neural network business.industry General Medicine computer.software_genre Industrial engineering Field (computer science) Power (physics) Reduction (complexity) Key (cryptography) Rough set Data mining Electric power industry business computer Energy (signal processing)
Zdroj:	Applied Mechanics and Materials. :3795-3799
ISSN:	1662-7482
DOI:	10.4028/www.scientific.net/amm.44-47.3795
Popis:	Power industry is the key field of implementing energy saving and pollutant emission reduction in china, strengthen power energy saving is helpful to establish a resource-saving and environment-friendly society and promote a sustainable development of economic society. This paper synchronizes respective advantages of rough set and neural network, puts forward a prediction model-RSBPNN which uses rough set knowledge reduction method to prune the redundant and neural network to build a forecasting model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b055f15eeec10e0e91af03032a5b8347 https://doi.org/10.4028/www.scientific.net/amm.44-47.3795 Zobrazit plný text záznamu