NDLI: Application of neural network in predication model of flotation indicators

Content Provider	IEEE Xplore Digital Library
Author	Yanqiong Tu Guanghua Ai Xiuxiang Tao Wangsheng Fang
Copyright Year	2011
Description	Author affiliation: School of Chemical Engineering and Technology; China University of Mining and Technology; Xuzhou, China (Xiuxiang Tao) \|\| School of Information Engineering; Jiangxi University of Science and Technology; Ganzhou, China (Yanqiong Tu) \|\| School of Resources and Environmental Engineering; Jiangxi University of Science and Technology; Ganzhou, China (Guanghua Ai) \|\| School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China (Wangsheng Fang)
Abstract	According to the floatation processing characteristic with time-variation, uncertainty and complicated nonlinear relations, a prediction method of concentrate grade and prediction model of ore dressing date is proposed. This article establish a prediction model of ore dressing date based on Jordan neural network including input of influence factors and dynamic time sequence feedback of concentrate grade, by combining BP algorithm with the temporal difference methods. The results applied in industry indicate that predictive precision is high, error is small, and stability is high. It has practical value, the application is successful.
Starting Page	196
Ending Page	199
File Size	97751
Page Count	4
File Format	PDF
ISBN	9781612848396
e-ISBN	9781612848402
DOI	10.1109/ICCRD.2011.5763893
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems TD method Heuristic algorithms Artificial neural networks Prediction model Predictive models Lead Prediction algorithms Neural network Ore dressing date Zinc BP algorithm
Content Type	Text
Resource Type	Article

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