Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network
WANG Hong-bing1,2,XU An-jun3,AI Li-xiang3,TIAN Nai-yuan3
1. School of Computer and Communication Engineering, University of Science and Technology Beijing,Beijing 100083, China 2. Key Laboratory of Advanced Control of Iron and Steel Process of Ministry ofEducation, Beijing 100083, China 3. School of Metallurgical and Ecological Engineering, University ofScience and Technology Beijing, Beijing 100083, China
Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network
WANG Hong-bing1,2,XU An-jun3,AI Li-xiang3,TIAN Nai-yuan3
1. School of Computer and Communication Engineering, University of Science and Technology Beijing,Beijing 100083, China 2. Key Laboratory of Advanced Control of Iron and Steel Process of Ministry ofEducation, Beijing 100083, China 3. School of Metallurgical and Ecological Engineering, University ofScience and Technology Beijing, Beijing 100083, China
ժҪ The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phosphorus content of molten steel in BOF (Basic Oxygen Furnace). At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calculated using EWM (Entropy Weight Method). At the predicting stage, one GMDH (Group Method of Data Handling) polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polynomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
Abstract��The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phosphorus content of molten steel in BOF (Basic Oxygen Furnace). At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calculated using EWM (Entropy Weight Method). At the predicting stage, one GMDH (Group Method of Data Handling) polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polynomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
WANG Hong-bing,,XU An-jun,AI Li-xiang,TIAN Nai-yuan. Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network[J]. �й������ڿ���, 2012, 19(1): 11-16.
WANG Hong-bing,,XU An-jun,AI Li-xiang,TIAN Nai-yuan. Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network. Chinese Journal of Iron and Steel, 2012, 19(1): 11-16.
2012, Vol. 19 
