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.
参考文献
| [1] | |
| [2] | Anupam Das, Maiti, J., Banerjee, R.N., 2010. Process control strategies for a steel making furnace using ANN with Bayesian regularization and ANFIS. Experts Systems with Applications, 37(2), 1075-1085.[2] Balakrishnan, P.V.S., Cooper, M.C., Jacob, V.S., Lewis, P.A., 1996. Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. European Journal of Operational Research 93(2), 346-357.[3] Chan, E.Y., Ching, W.K., Michael, K.N., Huang, J.Z., 2004. An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition 37(5), 943-952.[4] Frattini Fileti, A.M., Pacianotto, T.A., Pitasse Cunha, A., 2006. Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel. Engineering Applications of Artificial Intelligence 19(1), 9-17.[5] Gopalakrishnan, M., Sridhar, V., Krishnamurthy, H., 1995. Some applications of clustering in the design of neural networks. Pattern Recognition Letters 16(1), 59-65.[6] Han, M., Zhao, Y., 2011. Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine. Expert Systems with Applications. (In press)[7] Hsieh, N.C., 2005. Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications 28(4), 655-665.[8] Kim, Y.S., Street, W.N., 2004. An intelligent system for customer targeting: A data mining approach. Decision Support Systems 37(2), 215-228.[9] Kuo, R.J., Ho, L.M., Hu, C.M., 2002. Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers and Operations Research 29(11), 1475-1493.[10] Li, C.R., Zhao H.W., Xie X., Yin Q., 2011. Prediction of End-Point Phosphorus Content for BOF Based on LM BP Neural Network. Iron and Steel 46(4), 23-25, 30.[11] Lin, Z.Z., Wen, F.S., Huang, J.S., Zhou, H., 2010. Evaluation of black start schemes employing entropy weight-based decision-making theory. Journal of Energy Engineering 136(2), 42-49.[12] Liu, J., Yuan S.Q., Li, J.G., 2005. Prediction of End Phosphorus in Converter Steelmaking Using Optimized BP Network Based on SPR. Journal of Iron and Steel Research 17(3), 65-67, 78.[13] Rajesh, N., Khare, M.R., Pabi S.K., 2010. Feed forward neural network for prediction of end blow oxygen in LD converter steel making. Materials Research, 13(1), 15-19.[14] Rissanen, J., 1978. Modeling by shorted data description. Automatica 14(5), 465-471.[15] Salih, G., Kemal, P., Sebnem, Y., 2010. Efficient sleep stage recognition system based on EEG signal using K-means clustering based feature weighting. Expert Systems with Applications 37(12), 7922-7928.[16] Sugeno, M., 1995. Industrial Applications of Fuzzy Control. NY: Elsevier Science Pub Co.[17] Sugiura, N., 1978. Further analysis of the data by Akaike’s information criterion and the finite corrections. Communication in statistics, theory and methods A7, 13-26.[18] Sung, A.H., 1998. Ranking importance of input parameters of neural networks. Experts Systems with Applications 15(03), 405-411.[19] Tsai, C.Y., Chiu, C.C., 2008. Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm. Computational Statistics and Data Analysis 52(10), 4658-4672.[20] Varahrami, V., 2011. Recognition of good prediction of gold price between MLFF and GMDH neural network. Journal of Economics and International Finance 3(4), 204-210.[21] Xie, S.M., Tao, J., Chai, T.Y., 2003. Intelligent method for BOF endpoint phosphorus estimation. Control Theory and Applications 20(4), 555-559.[22] Yang, L.H, Liu, L., He, P., 2002. Prediction and Control Model of End Phosphorus Content for Oxygen-Converter Based on Adaptive Neutro-Fussy System. Iron and Steel 14(4), 47-51.[23] Yi, Q.M., 2008. Blind source separation by weighted K-means clustering. Journal of Systems Engineering and Electronics 19(5), 882-887.[24] Zou, Z.H., Yun, Y., Sun, J.N., 2006. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences 18(5), 1020- 1023. |
上一张
下一张
上一张
下一张
计量
- 下载量()
- 访问量()
文章评分
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%