Application of neural network in predication model of flotation indicators

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 n...

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Bibliographic Details
Main Authors: Yanqiong Tu, Guanghua Ai, Xiuxiang Tao, Wangsheng Fang
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
BP algorithm
Heuristic algorithms
Lead
Neural network
Ore dressing date
Prediction algorithms
Prediction model
Predictive models
Real time systems
TD method
Zinc
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Summary: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.
DOI:10.1109/ICCRD.2011.5763893