中国有色金属学报(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 10 No. 6 December 2000 |
through point based on self-organize artificial neural network①
（Department of Mineral Engineering, Central South University, Changsha 410083, P.R.China）
Abstract:A soft-sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self-organize the hidden-layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.
Key words: sintering process; burning through point; prediction; artificial neural network; BP algorithm
