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.
Author: Song Qiang, Wu
Yao-chun
Journal Code: jptkomputergg160164
