Prediction Model of Smelting Endpoint of Fuming Furnace Based on Grey Neural Network

Abstract: Since grey theory and neural network could improve prediction precision, the technology of combination prediction was proposed in this study. Then the algorithm was simulated by Matlab using practical data of a fuming furnace. The results reveal that the smelting endpoint of fuming furnace could be accurately predicted with this model by referring to small sample and information. It shows that the GNN algorithm not only has strong global search capability, but also is easy to implement. A Smelting Endpoint of Fuming forecasting empirical example has shown that compared with back-propagation artificial neural networks and single gray theory algorithm, GNN algorithm can achieve higher prediction accuracy, better computational speed, and which is more suitable for Prediction of Smelting Endpoint of Fuming forecasting. Therefore, GNN model is effective with the advantages of high precision, fewer samples required and simple calculation.
Keywords: smelting endpoint, gray neural network, prediction, sintering process, gray model
Author: Song Qiang, Wu Yao-chun
Journal Code: jptkomputergg160164

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