[1] [1] Feng Jie, Zhang Hongwen. BOF Steelmaking[M]. Beijing: Metallurgical Industry Press, 2006. 332～338

[2] [2] Daniel M. Mass spectrometry for oxygen steel-making control[J]. Steel Times, 1997, 225(11): 439～440

[3] [3] Sharan A. Light sensors for BOF carbon control in low carbon heats[C]. Steelmaking Conference Proceedings, 1998, 81: 337～345

[4] [4] Wang Yong, Yang Ningchuan, Wang Chengkuan. Present situation and development of converter steelmaking in China[J]. Special Steel, 2005, 26(4): 1～5

[5] [5] Feng Mingxia, Li Qiang, Zou Zongshu. An outlier identification and judgment method for an improved neural-network BOF forecasting model[J]. Steel Research International, 2008, 79(5): 323～331

[6] [6] Xie Shuming, Tao Jun, Chai Tianyou. BOF steelmaking endpoint control based on network[J]. Control Theory and Applications, 2003, 20(6): 903～907

[7] [7] Cemalettin K, Harnun T, Recep A et al.. Bofy-fuzzy logic control for the basic oxygen furnace (BOF)[J]. Robotics and Autonomous System, 2004, 49(3～4): 193～205

[8] [8] Smith A R. Color gamut transform pairs[J]. Computer Graphics, 1978, 12(3): 12～19

[9] [9] Chen Chulung, Wu Weijun. Color pattern recognition with the multi-channel non-zero-order joint transform correlator based on the HSV color space[J]. Opt. Commun., 2005, 244(1～6): 51～59

[10] [10] Zhao Jianhua, Fang Jun, Shu Xueming. An idetification method of fire smoke based on neural network[J]. Acta Optica Sinica, 2003, 23(9): 1086～1089

[11] [11] Tang Yan, Chen Wenjing. Neural network applied to three-dimensional measurement of complex objects[J]. Acta Optica Sinica, 2007, 27(8): 1435～1439

[12] [12] Du Xiliang, Dai Jingmin. Data processing method for the division-of-amplitude photopolarimeter based on an artificial neural network[J]. Chin. J. Lasers, 2007, 34(1): 89～93

[13] [13] Qi Feng, Liu Wenqing, Zhou Bin et al.. Improving DOAS system measurement precision with artificial neutral network method[J]. Acta Optica Sinica, 2002, 22(11): 1345～1349

[14] [14] Chen Kaizhou. Optimized Arithmetic[M]. Xi′an: Northwest Institute of Telecommunication Engineering Press, 1985. 83～87

[15] [15] Castro J L, Mantas C J, Benitez J M. Neural networks with a continuous squashing function in the output are universal approximators[J]. Neural Networks, 2000, 13(6): 561～563