摘要
高炉冶炼过程是一个高度复杂的过程，其运行机制往往具有非线性、大时滞、大噪声、分布参数等特征。同时，高炉本身是一个集传热与化学反应耦合的开放体。在智能控制论范畴内解析高炉冶炼过程，并最终实现高炉冶炼过程的智能控制，是冶金科技发展的前沿课题。
本文选取具有代表性的2座中型高炉(莱钢1号750m~3高炉和临钢6号380m~3高炉)为研究对象。
论文前3章是在炼铁专家知识和模糊数学理论的基础上，从模糊数学角度详细分析了高炉冶炼过程的复杂性，并且由此得出结论：高炉冶炼过程不仅存在着许多非常规问题(如，炉况预报，异常炉况诊断、高炉设备诊断等)，而且在对其进行分析判断的过程(炉温[Si]辨识、预测及控制等)中存在着大量的模糊信息。这些非常规问题和模糊信息无法用简单的方式作出确切的描述，而必须采用模糊数学中的隶属函数对其中的不确定知识进行量化处理。文中对许多变量建立了隶属函数，采用了多种形式的隶属函数方法，例如，利用3维隶属函数法确定了铁水温度及其可信系数间的相互关系。3维隶属函数法有助于减少规则数目；利用非参数相似度法对[Si]进行模糊聚类分析，建立相应的模糊相似关系矩阵，并用模糊熵评价聚类效果。
随着智能控制理论研究的不断深入，建立功能更强的异常炉况预报与诊断模型来改善和提高模型的预测准确度和诊断效果，成为新的期望目标。本文第4章从炼铁工艺实际数据出发，深入分析了影响炉况的几个关键参数，按照这些参数的不同波动情况进行了定量的模糊分类。在此基础上，建立了异常炉况模糊预报模型和异常炉况模糊诊断模型。这2个模型不仅可以对中间过程进行逻辑推理，而且能定量计算高炉异常炉况的符合程度以及发展趋势。模拟结果表明，异常炉况模糊预报和诊断模型具有较好的可操作性。
高炉炼铁过程的炉温预测与控制的数学模型研究，既是炼铁自动化中的难题，也是实际炼铁生产中工长和厂长都十分关注的重要课题。炉温的准确预报，将有助于工长提高操作水平，从而达到提高利用系数和降低焦比的目的。论文第5章结合莱钢1号高炉和临钢6号在线采集的生产数据，针对高炉冶炼过程存在的时滞，应用广义函数分析法，计算了主要检测参数的时滞。基于模糊系统和神经网络的逼近特性，将模糊数学理论、随机系统理论以及神经网络方法结合在一起，建立了一整套的高炉炉温[Si]的智能预报模型，并进行横向和纵向的对比研究。仿真结果表明，该套模型不仅能够很好的预测炉温[Si]，而且能指导高炉工长调控炉温，使之处于最佳状态。
高炉冶炼过程控制是高炉主控室指挥下的多目标、多工序的复合控制。炉温控制是高炉冶炼过程控制的核心和基础。“炉温控制”不同于传统意义上的“温度控制”，其区别在于：“炉温控制”不仅与提供的能量有关，而且与物理化学反应过程、流体相态有关。论文第6章在逆系统理论的基础上，将喷煤量作为主控量，建立高炉炉温[Si]模糊预测控制模型和炉温[Si]的聚类模糊控制模型，在模糊控制论范畴内，探索高炉炉温[Si]控制规律。
Blast Furnace (BF) ironmaking process is highly complicated, whose operating mechanism is characteristic of nonlinearity, time lag, high dimension, big noise and distribution parameter etc. What's more, BF is an open system with heat transport and chemical reaction coupling. Study on BF ironmaking process with intelligent cybernetics and realization of intelligent control to it are the frontier in the field of metallurgic development.
In present dissertation, No.1 BF (750m~3) at Laiwu Iron and Steel Group Co. and No.6 BF(380m~3) at Linfen Iron and Steel Co., which are the representation of medium-sized BF in China, were selected out as the studying objective.
The complexity of BF ironmaking process was analyzed in detail in the first three chapters of this article, based on Fuzzy Mathematics and knowledge of ironmaking experts. The following conclusions were drawn: Not only many unconventionality problems (including prediction and diagnosis of abnormal BF state, diagnosis of BF equipment etc) exist in BF ironmaking process, but much fuzzy information is encountered in analysis BF ironmaking process (for example, identification, prediction and control of [Si]), which makes Simple-inference disabled, Membership Functions in Fuzzy Mathematics must be used to solved those problems. Many kinds of Membership Functions were constructed, for example, the relationship between hot metal temperature and it's confidence coefficients was confirmed with the method of three-dimensional membership function, and the amount of rules can be reduced through three-dimensional membership function. Nonparametric similar degree method was used in the fuzzy cluster of [Si], and the fuzzy similarity relation matrix of [Si] was presented, with the fuzzy entropy as the evaluation criterion.
With the development of intelligent cybernetics, new and more efficient prediction and diagnosis models of abnormal BF state should be established on intelligent cybernetics, and it is the object of ironmaking process. Those models were presented in the fourth chapter of this paper. Some key parameters of BF state were analyzed in detail, based on fuzzy mathematics. And then, Fuzzy prediction model and fuzzy neural net diagnostic model of BF state were presented. Not only the internal logical-inference, but quantitative calculation of conformity and variation trend of BF state was given with those two fuzzy models, and they are operational models.
Study on prediction and control models of temperature in BF is the most difficult problem in automation of BF ironmaking process and practical production. The exact
引文
[1] 国家自然科学基金委员会．自动化科学与技术—自然科学学科发展战略调研报告[M]．科学出版社，1995．
[2] 国家科技部．《国家级科技成果重点推广计划》项目证书．项目编号：99040422A，证书编号：2761．
[3] Li Qihui，Liu Xiangguan，Fuzzy Prediction for BF Hot Metal Silicon Content[J]．Journal of Iron and Steel Research International，录用待发表．
[4] Liu Xiangguan, Li Qihui, Intelligent Automation Models for BF Ironmaking Process, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), v 4, 2004, p 3547-3551.
[5] 周传典．高炉炼铁生产技术手册[M]．冶金工业出版社，2002．
[6] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．冶金工业出版社，2003．
[7] 周明，秦民生．高炉铁水含硅预报数学模型[J]．钢铁，1986；21(5)：7．
[8] Van langen J M. Blast Furnace Technology[M]. New York, 1972.
[9] Pandit S M, Clum J A, Wu S M. Modeling, prediction and control of blast furnace operation from observed data by multivariate time series[C]. Ironmaking Proceedings, Metallurgical Society of AIME, Iron and Steel Division, 1975; 34: 403.
[10] 姬田冒孝，西尾通卓，西川洁等．统计制御理论高炉炉热制御适用[J]．铁钢，1980；S96．
[11] Singh H, Sridhar N V, Deo B. Artificial neural nets for prediction of silicon content of blast furnace hot metal[J]. Steel Research, 1996; 67(12): 521.
[12] Yao B, Yang T J, Ning X J. An improved artificial neural network model for predicting silicon content of blast furnace hot metal[J]. Journal of University of Science and Technology Beijing, 2000; 7(4): 269
[13] 高小强，郑忠，黄庆周．高炉铁水含硅量和含硫量动力学预报研究[J]．钢铁，1995；30(4)：10．
[14] Miyano T, Kimoto S, Shibuta H, et al. Time series analysis and prediction on complex dynamical behavior observed in a blast furnace[J]. Physica D, 2000; 135(3-4): 305.
[15] 刘学艺，基于贝叶斯网络的高炉炉温[Si]预测控制模型研究[D]．浙江大学硕士学位论文，2004．
[16] 郜传厚，高炉冶炼过程的混沌动力学研究[D]．浙江大学博士毕业学位论文，2004．
[17] 岡部侠児，福武刚，高橋博保等．GO-STOP高炉安定操業[N]．川崎製铁技报，1979；11(1)：34．
[18] Pekka I, Antti K, Matti S. Computer systems for controlling blast furnace operations at Rautaruukki[J]. Iron and Steel Engineer, 1995; 72(8): 44
[19] Druckenthaner H. Blast furnace automation for maximizing economy[J]. Steel Times International, 1997; 21(1): 16.
[20] 刘云彩，张宗民，庄玉茹等．首钢2号高炉冶炼专家系统的开发与应用[J]．炼铁，1995；14(6)：31．
[21] 安云沛，车玉满，刘方等．鞍钢4号高炉热状态专家系统的开发与应用[J]．鞍钢技术，1997；(8)：6．
[22] 王荣贵．马钢2500m~3高炉自动化控制系统设计与调试要点[J]．钢铁设计，1994；(4)：6．
[23] 刘祥官，刘芳，刘元和等．莱钢1号750m~3高炉智能控制专家系统[J]．钢铁，2002；37(8)：18．
[24] 肖水枢，模糊数学基础及应用[M]．航空工业出版社．1992．
[25] 刘金琨．人工智能在高炉炉况判断中的理论与应用研究[J]．系统工程．1998，16(1)．71-72．
[26] 周冠五，杨天均，刘云彩等，《利用人工智能高炉冶炼专家系统的方法》，中国专利：93108671．X．[1] 周冠五，杨天均．刘云彩等，利用人工智能高炉冶炼专家系统的方法，中国专利：93108671．X，1998．
[2] L.A.Zadeh, Fuzzy sets, Information and Control, Vol. 8, 1965, 338-353.
[3] 杨伦标，高英仪，模糊数学·原理及应用(第二版)，华南理工大学出版社，1998．
[4] 王熙照，哈明虎，模糊示例学习与模糊控制，河北大学出版社，2002．
[5] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．冶金工业出版社，2003．
[6] 秦民生，杨天均．炼铁过程的解析与模拟[M]．冶金工业出版社，1991．
[7] 何清，模糊聚类分析理论与应用研究进展．模糊系统与数学，1998，12(2)：89-94．
[8] Ruspini E.H,A new approach to clustering.Inf Control.1969. 15 22-32.
[9] Tamura S, Higuchi S, Tanaka K. Pattern classification based on fuzzy relations. IEEE SMC,1971,1(1): 217-242.
[10] Zkim Le. Fuzzy relation compositions and pattern recognition. Inf Sci, 1996, 89: 107-130.
[11] Wu Z, Leathy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE PAMI, 1993, 15(11): 1101-1113.
[12] Krishnapuram R, Kill J M. A possibilistic approach to clustering. IEEE Fuzzy System, 1993, 1(2): 98-110
[13] Selim S Z, Ismail M A. Soft clustering of multidimensional data: a semifuzzy approach. Pattern Recognition, 1984, 17(5): 559-568.
[14] Kamel M S, Selim S Z. A threshold fuzzy c-means algorithm for semifuzzy clustering. Pattern Recognition, 1991, 24(9): 825-833.
[15] 裴继红，范九伦，谢维信．一种新的高效软聚类方法：截集模糊c-均值聚类算法．电子学报，1998，26(2)：83-86．
[16] 高新波，谢维信．模糊聚类理论发展及应用的研究进展，科学通报，1999，44(21)．
[17] Anderberg M R. Cluster Analysis for Applications. New York: Academic Press, 1973.[18] var Ryzin J. Classification and Clustering. New York: Academic Press, 1977.
[19] Dubes R C, Jain A K. Algorithms for Clustering Data. Englcwood Cliffs, NJ: Prentice Hall, 1988.
[20] 李相镐, 李洪兴, 陈世权, 等. 模糊聚类分析及其应用. 贵阳: 贵州科学技术出版社. 1994.
[21] Trauwaert E, Kaufman L, Rousseeuw P. Fuzzy clustering algorithms based on the maximum likelihood principle. Fuzzy Sets and System, 1991,85(42): 213-227.
[22] Li R P, Mukaidino M. A maximum entropy approach to fuzzy clustering. IEEEFUZZ'95,1995: 2227-2232.
[23] Krishnapuram R, Kim J W. A clustering algorithm based on minimum volume. IEEEFUZZ'96, 1996:1387-1392.
[24] Liaw J N, Kashyap R L. A new sequential classifier using information criterion window. Pattern Recognition,1994,27(10): 1423-1438.
[25] Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well separated cluster. J Cybernet, 1974,3: 32-57.
[26] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981.
[27] Bobrowski L, Bezdek J C. c-Means clustering with the l_1 and l_∞ norms. IEEE SMC, 1991,21(3): 545-554.
[28] Yuan B, Klir G J, Swan-Stone J F. Evolutionary fuzzy c-means clustering algorithm. IEEE FUZZ'95, 1995:2221-2226.
[29] Hathaway R J, Davenport J W, Bezdek J C. Relational duals of the c-means clustering algorithms. Pattern Recognition, 1989, 22(2): 205-212.
[30] Yang M S, Pan J A. On fiizzy clustering of directional data. Fuzzy Sets and Systems, 1997,91(3): 319-326.
[31] Yang M S, Ko C H. On a class of fuzzy c-numbers clustering problems for fuzzy data. Fuzzy Sets and Systems, 1996,84.49-60.
[32] Sonbaty Y E, Ismail M A. Fuzzy clustering for symbolic data. IEEE Fuzzy Systems, 1998, 6(2): 195-204.
[33] Pezdrcy P. Algorithms of fuzzy clustering with partial supervision. Pattern Recognition Letters, 1985,3:13-20.
[34] Bensaid A M, Hall L Q, Bezdek J C, et al. Partially supervised clustering for image segmentation. Pattern Recognition, 1996,29(5): 859-871.
[35] Dave R N, Krishnapuram R. Robust clustering methods: A unified view. IEEE Fuzzy Systems, 1997,5(2): 270-293.
[36] Gustafson E E, Kessel W C. Fuzzy clustering with a fuzzy covariance matrix. Proc IEEE CDC San Diego CA, 1979: 761-766.
[37] Jawahar C V, Biiswas P K, Ray A K. Detection of distinct geometry: a step towards generalized fuzzy clustering. Pattern Recognition Letters, 1995,16: 1119-1123 [23] Bezdek J C. A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE PAMI, 1980,1(2): 1-8.
[38] Chaudhuri D, Chaudhuri B B. A novel multiseed nonhierarchical data clustering technique. IEEE SMC, 1997,27(5): 871-877.
[39] Postaire J G, Zhuang R D, Lecocq-Botte C. Cluster analysis by binary morphology. IEEE PAMI, 1993,15(2): 170-180.
[40] Kohonen T. Selforganization and Associative Memory. Berlin: Springerverlag, 1984.
[41] Grossberg S. Nonlinear neural networks: principles, mechanisms, and architectures. Neural Networks, 1988,1: 17-61.
[42] Buckles B P, et al. Fuzzy clustering with genetic search. IEEE FUZZ'94, 1994:46-50.
[43] Babu G P, Murty M N. Clustering with evolution strategies. Pattern Recognition, 1994,2(27): 321-329.
[44] ALSultan K S, Fediji C A. A tabu searchbased algorithm for the fuzzy clustering problem. Pattern Recognition, 1997, 12(30): 12023-12039.
[45] Dubes R C, Jain A K. Validity studies in clustering methodologies. Pattern Recognition, 1979,11: 235-254.[46] Windham M P. Cluster validity for fuzzy c-means clustering algorithms. IEEE PAMI, 1982, 4(4): 357-363.
[47] Gunderson R. Applications of fuzzy ISODATA algorithms startracker printing systems. In: proc 7th Triennial World IFAC Congr, Helsinki, Finland, 1978: 1319-1323.
[48] Xie X L, Beni G. A validity measure for fuzzy clustering. IEEE PAMI, 1991, 13: 841-847.
[49] Vogel M A, Wong A C. PFS clustering method. IEEE PAMI, 1979, 3: 237-245.
[50] Jain A K, Moreau J V. Bootstrap techniques in cluster analysis. Pattern Recognition, 1987, 20(5): 547-563.
[51] Beni C, Liu X M. A least biased fuzzy clustering method. IEEE PAMI, 1992, 16(9): 954-960.
[52] Ramdas V, Sridhar V, Krishna G. An effective technique for feature extraction. Pattern Recognition Letters, 1994, 15: 885-891.
[53] Bezdek J C, Castelaz P F. Prototype classification and feature selection with fuzzy sets. IEEE SMC, 1977, 2(7): 87-92.
[54] Gao Xinbo, Xu Chunguang, Xie Weixin. Fuzzy partitioning of feature space for pattern classification based on supervised clustering. IEEE ISPACS'98, Melbourne, 1998: 387-391.
[55] Coleman G B, Andrews H C. Image segmentation by clustering. Proc IEEE, 1979, 5(67): 773～785
[56] 刘健庄．基于二维直方图的图像模糊聚类分割方法．电子学报，1992，20(9)：40-46．
[57] Trivedi M M, Bezdek J C. Low-level segmentation of aerial image with fuzzy clustering. IEEE SMC, 1986, 16(4): 589-598.
[58] Porter R, Canagarajah N. A robust automatic clustering scheme for image segmentation using wavelets. IEEE Image Processing, 1996, 5(4): 662-665.
[59] Chandhuri B B, Sarkar N. Texture segmentation using fractal dimension. IEEE PAMI, 1995, 17(1): 72-77.
[60] Shih F Y, Moh Jenlong, Chang Fuchun. A new artbased neural architecture for pattern classification and image enhancement without prior knowledge. Pattern Recognition, 1992, 25(5): 533-542.
[61] Lai Weichi. A VLSI neural processor for image data compression using self-organization networks. IEEE Neural Networks, 1993, 3(3): 506-517.
[62] 蒋祖华，吴群，模糊规则类知识管理及实践，计算机工程与应用，2004，40(9)．107-108．
[63] H Ishibuchi,H Tanaka. Neural Network That Learn from Fuzzy if-then Rules[J] IEEE Trans Fuzzy Syst, 1993, 12: 85-97.
[64] Matthews C, Jagielska I. Fuzzy Rules Extraction from a Trained Multilayered Neural Network[A]. IEEE Int'1 7th Conf on Neural Network[C]. 1995.744-748.
[65] A Grauel, H Mackenberg. Mathematical Analysis of the Sugeno Controller Leading to General Design Rules[J]. Fuzzy SetsSyst, 1997, 85(2): 165-175.
[66] T P HiTlg, C Y Lee. Induction of Fuzzy Rules and Membership Functions from Training Examples. Fuzzy Sets Syst, 1996, 84(1): 33-47.
[67] 黄晓斌，万建伟，张燕，一种基于模糊规则的分类方法，计算机工程与科学，2004，26(3)．57-60．
[68] Berenji, H. R.. An Introduction to Fuzzy logic Application in Intelligent System. New York Kluwer Academic Publishers, 1992.69-96.
[69] 李人厚，张平安，关于模糊辨识的理论与应用实际问题，1995，12(2)．129-137．
[70] Pedrycz W.. An Identification Algorithm in Fuzzy Relational Systems. Fuzzy Sets and Systems, 1984,13:153-167.
[71] Tomohiro,T. and Sugeno, M.. Fuzzy Identification of System and Its Application to Modeling and Control. IEEE Trans.on SMC、1985,15. 116-132.
[72] Horikawa, S.I. and Takeshi,F.. On Fuzzy Modeling Using Fuzzy Neural Networks with the Back-Propagation Algorithm. IEEE Trans. On NN, 1992,3:801-806.
[73] Sugeno,M. and Nishida, M.. Fuzzy Control of Model Car. Fuzzy Seres and Systems, 1985,16:103-113.[74] 孙波，一种从操作人员的经验数据直接抽取模糊控制规则的方法．智能控制与智能自动化，1991，997-1001
[75] Sugeno, M and Takairo,Y.. A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Trans.on Fuzzy systems, 1993, 1: 7-31.
[76] Emest, C. and PedTycz, W.. On Identification in Fuzzy Systems and Its Applications in Control Problems. Fuzzy Sets and Systems.1981. 6:73-83.
[77] Emest, C.and Pedrycz, W.. Control Problems in Fuzzy Systems. Fuzzy Sets and Systems, 1982,7:257-273.
[78] Predrycz, W.. Fuzzy Control and Fuzzy Systems[M]. England, Research Studies Press LTD, 1989.
[79] Pedrycz, W.. Identification in Fuzzy Systems.IEEE Trans.on SMC,1984,14: 361-366.
[80] Bae,S. W.,Park, T. H. and Lee. K.S.. An On-Line Fuzzy Identification Method Utilizing Fuzzy Model Evaluation. Fifth IFSA world Congress,1993, 1227-1229.
[81] Hwang. H.S.and Young,H.J. Generalized Fuzzy Modeling. Fifth IFSA World Congress,1993. 1145-1150.
[82] Sun, C T., Rule-Based Structure Identification in an Adaptive—Network-Based Fuzzy Inference System. IEEE Trans.on Fuzzy Systems, 1994,2: 64-73.
[83] Yushinari, Y., Pedrycz, W. and Hirota, K.. Construction of Fuzzy Models Through Clustering Techniques. Fuzzy Sets and Systems, 1993,54:157-165.
[84] Pedrycz, W., Fuzzy Sets Engineering, Boca Raton: CRC Press,1995, Z9-70.
[85] Wang, L.X., Fuzzy systems are universal approximators [C], in Proc. IEEE Int. Conf. Fuzzy Systems, San Diego, CA, USA, 1992, 1163-1170.
[86] 金晓明，荣冈，王树青等．动态系统模糊辨识的新算法[J]．控制理论与应用．1997，14(4)．565-569．
[87] Koshy P. Vaidyan, Achuthsankar S. Nair and Thomas Kurian, Simulation of a control system using ANFIS as the controller. Proceeding of the Fifteenth Kerala Science Congress, Thiruvananthpuram, 2003,661-670.
[88] Tanaka H, Uejima S.and Asai K. Linear regression analysis with fuzzy model. IEEE Trans.on Systems. Man & Cyberneties,1982,12(6): 903-907.
[89] 陈怡欣，萧德云，一种基于扩张原理的模糊模型及其辨识方法[J]．自动化学报，1999，25(6)．743-749．
[90] Dubois D, Prade H. Fuzzy Sets and Systems, Theory and Applications. New York: Academlc Press,Inc.,1980, 36-66.
[91] Pawlak Z. Rough set-theoretical aspects of reasoning about data [M]. London: Kluwer Academic Publishers, 1991.9-30.
[92] Takagi,T. and Sugeno,M.. Fuzzy Identification of Systems and Its Application to Modeling and Control[J]. IEEE Trans.Syst, Man & Cybern, 1985,15:116-132.
[93] 汤兵勇，模糊模型的辨识及应用[M]．北京：中国环境科学出版社，1994．[1] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．北京：冶金工业出版社，2003．
[2] Li Qihui，Liu Xiangguan，Fuzzy Prediaion for BF Hot Metal Silicon Content[J]．Journal of Iron and Steel Research International，录用待发表．
[3] 由文泉，赵民革．实用高炉炼铁技术[M]，北京：冶金工业出版社，2002．
[4] 万谷志郎著．李宏译．钢铁冶炼[M]．北京：冶金工业出版社，2001．
[5] 成兰伯著．高炉炼铁工艺及计算[M]．北京：冶金工业出版社，1995．
[6] Lowing J. The diagnostic approach to overcoming blast furnace operation problems[C]. Ironmaking proceedings, Metallurgical Society of AIME, Iron and Steel Division, 1977; 36:212.
[7] 秦民生，杨天均．炼铁过程的解析与模拟[M]．北京：冶金工业出版社，1991．
[8] J.Sterneland, M.A.T. Andersson and P.G. Josson, Comparison of iron ore reduction in experimental blast furnace and laboratory scale simulation of blast furnace process, Ironmaking and Steelmaking, 2003, 30(4),313-327.
[9] Koshy P. Vaidyan, Achuthsankar S. Nair and Thomas Kurian, Simulation of a control system using ANFIS as the controller. Proceeding of the Fifteenth Kerala Science Congress, Thiruvananthpuram, 2003,661-670.
[10] 傅世敏，刘子久，安云沛．高炉过程气体动力学[M]．北京：冶金工业出版社，1990．
[11] 安云沛．1982年辽宁省金属学会烧结炼铁论文论文集[C]．1982，p：129．
[12] 刘秉铎．1982年辽宁省金属学会烧结炼铁论文论文集[C]．1982，p：14．
[13] 岩村忠昭．川绮制铁技报[J]．1981，13(4)．P：129
[14] 道叶晋一，福岛勤，八木顺一郎等．高炉内现象及其解析(鞍钢钢研所译)．北京：冶金工业出版社，1985．
[15] Jun-ichiro YAGI, Hiroshi NOGAMI and Mansheng CHU. Numerical analysis on blast furnace performance by multidimensional transient simulator based on multi-fluid theory. Third international conference in the minerals and process industries[C]. 2003, 441-448.
[16] Nicklas Eklund and Anna Dahlstedt. The choice of pellets in a mixed blast furnace burden and how it effects process conditions. The 14th conference on Hungarian pig iron and steel making. 2002 1-14.
[17] Bird, R., Stewart, W., and Lightfoot, E. Transport phenomena [M]. John Wiley Inc., New York, NY. 1960.
[18] Press, W., Teukolsky, S., Vetterling, W., etc. Numerical recipes in the art of scientific programming(2nd edition). Cambridge University Press. 1992.
[19] A.B. Yu, G.X. Wang and S.J. Chew. etc. Modeling the gas-liquid flow in an ironmaking blast furnace. Progress in computational fluid dynamics[J]. 2004, 4(1)29-38.
[20] 姜慧研，许桂清，周建常．BP算法在高炉煤气流分布模式识别中的应用[J]．东北大学学报(自然科学版)．2000，21(2)．144-146．
[21] Z. Y. Zhou, A.B. Yu and P. Zulli. A simplified mathematical model for gas-solid flow in a blast furnace. Progress in computational fluid dynamics[J]. 2004, 4(1) 39-45.
[22] 潘国友，杨乃伏，顾飞．高炉中心装焦时煤气、炉料流场的有限元法解析[J]．钢铁．1999，34(7)．5-11．[1] 周冠五，杨天均，刘云彩等，《利用人工智能高炉冶炼专家系统的方法》，中国专利：93108671．X．
[2] 钱敏，钟大鹏．2500m~3高炉炉况失常的分析[J]，马钢技术．2001，(3)．30-35，47．
[3] 邓新民．马钢2500m~3高炉炉况失常的处理[J]．炼铁．2002，21(4)．41-42．
[4] 刘云彩．当代高炉炼铁成就[J]．炼铁，2001，20(3)，27-
[5] 毕学工．人工智能和专家系统在钢铁工业中的应用[J]．武汉钢铁学院学报．1995，18(2)146-152．
[6] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．冶金工业出版社，2003．
[7] 刘金琨．人工智能在高炉炉况判断中的理论与应用研究[J]．系统工程．1998，16(1)．71-72．
[8] 由文泉，赵民革．实用高炉炼铁技术[M]，北京：冶金工业出版社，2002．
[9] 成兰伯著．高炉炼铁工艺及计算[M]．北京：冶金工业出版社，1995．
[10] 万谷志郎著．李宏译．钢铁冶炼[M]．北京：冶金工业出版社，2001．
[11] 安云沛译．千叶厂判断和控制高炉炉况的Go-Stop系统简介[J]．国外钢铁．1985．(1)11-20．
[12] 秦民生，杨天均．炼铁过程的解析与模拟[M]．北京：冶金工业出版社，1991．
[13] 吴胜利．刘茂林．基于模糊数学的高炉炉况预测模型[J]．钢铁．2001，36(3)．12-14，68．
[14] 王世同．模糊专家系统研究[J]．计算机研究与发展．1990，(7)．55-63．
[15] Joseph Giarratano and Gary Riley. Expert Systems Principles and Programming[M]. 1998, PWS Press.
[16] Koshy P. Vaidyan, Achuthsankar S. Nair and Thomas Kurian, Simulation of a control system using ANFIS as the controller. Proceeding of the Fifteenth Kerala Science Congress, Thiruvananthpuram, 2003,661-670.[1] 刘金琨．人工智能在高炉炉况判断中的理论与应用研究[J]．系统工程．1998，16(1)．71-72．
[2] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．冶金工业出版社，2003．
[3] 成兰伯著．高炉炼铁工艺及计算[M]．北京：冶金工业出版社，1995．
[4] Li Qihui, Liu Xiangguan, Fuzzy Prediction for BF Hot Metal Silicon Content [J]. Journal of Iron and Steel Research International, to be published.
[5] Liu Xiangguan, Li Qihui, Intelligent Automation Models for BF Ironmaking Process [C], Proceedings of the World Congress on Intelligent Control and Automation (WCICA), v 4, 2004, 3547-3551.
[6] Rao P, B. L. S. Identifiability in stochastic models: characterization of probability distributions[M]. Academic Press, 1992.
[7] Walter E. Identifiability of parmetrie models[M]. Pergamon Press, 1987.
[8] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems. Man and Cybernetics, 1985. 15(1): 116-132.
[9] Sugeno,M. and Yasukawa, T. A fuzzy-logic-based approach to qualitative modeling[JJ. IEEE Transactions on Fuzzy Systems. 1993, 1(1): 7-31.
[10] Oh S,Pedrycz. Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems[J]. Fuzzy Sets and Systems, 2000, 115: 205-230.
[11] Arnold, H. Fuzzy regression and the term structure of interest rates revisited [R]. AFIR2004_Shapiro_05. 2004,1-17.
[12] Tanaka, H., Uejima, S., and Asai, K. Linear regression analysis with fuzzy model [J]. IEEE Transactions on Systems, Man and Cybernetics. 1982,12(6). 903-907.
[13] Tanaka, H.. Fuzzy data analysis by possibilistic linear models [J]. Fuzzy Sets and Systems, 1987, 24, 363-375.
[14] Xu, R.. A linear regression model in fuzzy environment. Advances in Modeling and Simulation, 1991,27, 37-40.
[15] Izyumov, B., Kalinima, E. and Wagenknecht, M.. Software tools for regression analysis of fuzzy data [C]. Proceeding of 9th Zittau Fuzzy Colloquium. 2001, 221-229.
[16] Sánchez, J. de A. and Gómez, A.T.. Applications of fuzzy regression in actuarial analysis [J]. JRI. 2003 70(4), 665-669.
[17] Sánchez, J. de A. and Gómez, A.T.. Estimating a term structure of interest rates using fuzzy regression techniques [J]. European Journal of Operational Research. 2004, 154, 804-818.
[18] 彭祖赠，孙韫玉．模糊(Fuzzy)数学及其应用[M]．武汉：武汉大学出版社，2002．
[19] 金阵玉．信息论[M]．北京：北京理工大学出版社，1991．
[20] 丁晶，王文圣等．以互信息为基础的广义相关函数[J]．四川大学学报(工程科学版)．2002，34(3)，1-5．
[21] Diamond, P. Fuzzy least square. Information Science. 1988(46). 141-157.
[22] 许若宁．带模糊回归参数的线性回归模型[J]．模糊系统与数学．1998 12(2)．70-77．
[23] 陈怡欣，萧德云．一种基于扩张原理的模糊模型及其辨识方法[J]．自动化学报．1999，25(6)743-749．
[24] 秦民生，杨天均．炼铁过程的解析与模拟[M]．北京：冶金工业出版社．1991．
[25] Osamu Lida, Application of AI techniques to blast furnace operations, Iron and Steel Engineer, 1995, 24-28.
[26] Singh, H., Sridhar, N. V., and Deo, B., Artificial neural nets for prediction of silicon content of blast furnace hot metal [J], Steel Research 67 (1996) No. 12. 117-120.
[27] Mallick S., Basu K., Automation in blast furnace: scope and recent trends [J], Tata Search, 1998, 89-96.
[28] Radhakrishnan, V.R., and Mohamed, A.R., Neural networks for the identification and control of blast furnace hot metal quality [J], Journal of Process Control, 10(2000), No. 2, 509-518.
[29] Sterneland, J., and JOnsson, P.G, The use of coated pellets in optimizing the blast furnace operation[C], ISIJ??international, Vol.43(2003), No.1, 26-35.
[30] Emest, C. and Pedrycz, W., On identification in fuzzy systems and its application in control problems [J], Fuzzy sets and Systems, 1981, 6, 73-83.
[31] Wang, L.X., Fuzzy systems are universal approximators [C], in Proc. IEEE Int. Conf. Fuzzy Systems, San Diego, CA, USA, 1992, 1163-1170.
[32] Kosko, B., Fuzzy systems are universal approximators [C], in Proc. IEEE Int. Conf. Fuzzy Systems, San Diego, CA, USA, 1992, 1153-1162.
[33] 金晓明，荣冈，王树青等．动态系统模糊辨识的新算法[J]．控制理论与应用．1997，14(4)．565-569．
[34] Wang L.X. Adaptive fuzzy systems and control. Design and stability analysis[M]. Englewood Cliffs, NJ: FrrR Prentice-Hall, 1994.
[35] Jing Z.L.,LUO A. C.and Tomizuka, M.. A stochastic, fuzzy, neural network for nolinear dynamic systems[J]. Int Journal of Intelligent Control and Systems. 1998,3(2). 193-203.
[36] Liu Yingming. Proc of Int Conf on Fuzzy Information Processing Theories and Applications [C]. Beijing: Tsinghua University Press&Springer,2003. 497-502.
[37] 王军平，陈全世，田光宇．随机模糊神经网络及在随机混沌时间序列预测中的应用[J]．西安交通大学学报．2003，37(10)．991-994．[1] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统[M]．北京：冶金工业出版社，2003．
[2] 李士勇，模糊控制·神经控制和智能控制论[M]．哈尔滨：哈尔滨工业大学出版社，2004．
[3] 刘学艺．基于贝叶斯网络的高炉炉温[Si]预测控制模型研究[D]．浙江大学硕士学位论文，2004．
[4] 彭祖赠，孙韫玉．模糊(Fuzzy)数学及其应用[M]．武汉：武汉大学出版社，2002．
[5] 秦民生，杨天钧．炼铁过程的解析与模拟[M]．北京：冶金工业出版社．1991．
[6] Richalet, J. Industrial application of model based predictive control [J]. Autotalica, 1993, (29). 1251-1274.
[7] KUNTZE, H. B. On the predictive functional control of a classical industrial robot[A]. Proc, 25th CDC, Athens, Greece[C]. 1986. 1877-1881.
[8] RICHALET,J. Predictive functional control: application to fast and accurate robots[A]. IFAC 10th World Congress, Munich, PRG[C]. 1987.251-258.
[9] 舒迪前．预测控制系统及应用[M]．北京：机械工业出版社．1996．[1] ISIJ, B.F. Phenomena and Modeling, Elsevier Applied Science, London and New York, 1987.
[2] 李明树，唐朔飞，石森等．实时产生式系统．自动化学报，1994，20(3)．371-374．
[3] 刘祥官，刘芳．高炉炼铁过程优化与智能控制系统．北京冶金工业出版社．2003
[4] 周传典．鞍钢炼铁技术的形成与发展．北京：冶金工业出版社，1999．[5] T.N. Chang, M.W. Wu and J.C. Chiou, "Blast Furnace System Identification by Maximum Likelihood Method", IFAC Automation in Mining Mineral and Metal Processing, Tokyo, Japan, 1986, 229-234.
[6] S.K. Mallick Basu, "Automation in Blast Furnace: Scope and Recent Trends". Tata Search. 1998, 89-96.
[7] Li Qihui, Liu Xiangguan, Fuzzy Prediction for BF Hot Metal Silicon Content[J]. Journal of Iron and Steel Research International, to appear.
[8] Liu Xiangguan, Li Qihui, Intelligent Automation Models for BF Ironmaking Process, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), v 4, 2004, p 3547-3551.