隧道窑控制系统设计及其温度控制研究
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摘要
隧道窑作为耐火材料制品生产的重要设备,其温度控制水平是制约耐火材料质量提高的一个重要因素。因此温度控制策略是隧道窑控制系统中的重要研究内容。如何设计满足生产要求的隧道窑控制系统及制定先进的温度控制策略来提高产品质量、降低能耗、减少环境污染,成为工业窑炉控制领域一个迫切需要解决的问题。
本文以实际项目为背景,根据隧道窑生产过程的要求,在详细分析隧道窑工艺特点的基础上,设计出一套集回路控制、过程监控和管理信息系统于一体的隧道窑控制系统。该系统采用西门子PLC实现系统的回路控制,采用WinCC组态软件实现系统的监控管理功能。
为了研究先进的隧道窑温度控制策略,首先必须建立隧道窑温度模型。由于隧道窑工况复杂,难以用精确的数学模型来描述。本文以现场数据为基础,采用BP神经网络对窑温模型进行辨识。仿真结果表明基于BP神经网络的隧道窑温度模型辨识效果良好。
隧道窑是一个复杂的控制对象,具有大滞后、时变及非线性的特性,这些特性对于隧道窑温度的控制都是十分不利的。采用传统PID控制策略来控制隧道窑的温度,由于参数不易整定、抗干扰能力差、自适应性差、严重依赖操作人员的现场经验等一系列的缺点,达不到理想的控制效果。本文以BP神经网络辨识的窑温模型为被控对象,根据隧道窑温度控制的要求,设计一种基于模糊神经网络的温度控制器。该控制器具有清晰的网络结构,它利用神经网络的学习能力在线调整网络的参数,从而增强模糊控制的在线学习能力。通过MATLAB仿真实验可以看出：与基于PID的窑温控制系统相比,基于模糊神经网络的窑温控制系统对窑温的变化具有更好的跟随性,调整时间较短,而且具有较强的抗扰性和鲁棒性,能够满足隧道窑温度控制的要求。
The tunnel kiln is an important equipment which is used for burning refractory materials, and the control level of the temperature has the direct effect on the quality of products. So, how to design control system and good temperature control strategy for tunnel kiln which can satisfy the produce requirements, enhance the quality of the product, reduce the energy consumption and reduce the pollution, becomes a question which is need to be solved imminency.
The paper takes the practical project as the background, at first, according to the requirement of the produce process of tunnel kiln and detailed analysis of the demand and the characteristic of tunnel kiln, we design control system for tunnel kiln which contain loop control, process supervising and management information system. We use the PLC of Simense to take the loop control of the system and use the configuration software of WinCC to carry out the supervisory function of the system.
We need to establish the tunnel kiln temperature model before researching the advanced control strategy. Because the tunnel kiln is a complicated object which is hard to establish an exact mathematic model, so this paper identifies the dynamic model of the tunnel kiln temperature object through BP neural network based on the data from field. After making the matlab simulative experiment, we get the conclusion that the effect of the tunnel kiln temperature model identified by BP neural network is good.
The tunnel kiln is a complicated object which has some characteristics, such as, hysteretic property, time-varying and non-linear, these charactertisics causing as to its temperature control more difficult. The conventional PID control technique can't get the perfect control effect because the three parameters of PID can't be set easily, the anti-interference and adapting ability of PID are week, and it depends on the operators' experience. In this paper, we take the tunnel kiln temperature model identified by neural network as an object to be controlled. According to the requirement of the temperature control, we design a kind of new controller based on fuzzy neural network. The controller has a distinct network structure. The learning ability of the neural network is used to revise the parameters of neural to enhance the learning ability of the fuzzy control. At last, making the matlab simulative experiment, we can make the conclusion that kiln temperature control system based on fuzzy neural network has better performance than the kiln temperature control system based on PID in the following aspects:better following performance for temperature varying, less adjusting time, greater anti-interference ability and greater robustness, so, the fuzzy neural network controller can meet the requirements of tunnel kiln temperature control.
本文以实际项目为背景,根据隧道窑生产过程的要求,在详细分析隧道窑工艺特点的基础上,设计出一套集回路控制、过程监控和管理信息系统于一体的隧道窑控制系统。该系统采用西门子PLC实现系统的回路控制,采用WinCC组态软件实现系统的监控管理功能。
为了研究先进的隧道窑温度控制策略,首先必须建立隧道窑温度模型。由于隧道窑工况复杂,难以用精确的数学模型来描述。本文以现场数据为基础,采用BP神经网络对窑温模型进行辨识。仿真结果表明基于BP神经网络的隧道窑温度模型辨识效果良好。
隧道窑是一个复杂的控制对象,具有大滞后、时变及非线性的特性,这些特性对于隧道窑温度的控制都是十分不利的。采用传统PID控制策略来控制隧道窑的温度,由于参数不易整定、抗干扰能力差、自适应性差、严重依赖操作人员的现场经验等一系列的缺点,达不到理想的控制效果。本文以BP神经网络辨识的窑温模型为被控对象,根据隧道窑温度控制的要求,设计一种基于模糊神经网络的温度控制器。该控制器具有清晰的网络结构,它利用神经网络的学习能力在线调整网络的参数,从而增强模糊控制的在线学习能力。通过MATLAB仿真实验可以看出：与基于PID的窑温控制系统相比,基于模糊神经网络的窑温控制系统对窑温的变化具有更好的跟随性,调整时间较短,而且具有较强的抗扰性和鲁棒性,能够满足隧道窑温度控制的要求。
The tunnel kiln is an important equipment which is used for burning refractory materials, and the control level of the temperature has the direct effect on the quality of products. So, how to design control system and good temperature control strategy for tunnel kiln which can satisfy the produce requirements, enhance the quality of the product, reduce the energy consumption and reduce the pollution, becomes a question which is need to be solved imminency.
The paper takes the practical project as the background, at first, according to the requirement of the produce process of tunnel kiln and detailed analysis of the demand and the characteristic of tunnel kiln, we design control system for tunnel kiln which contain loop control, process supervising and management information system. We use the PLC of Simense to take the loop control of the system and use the configuration software of WinCC to carry out the supervisory function of the system.
We need to establish the tunnel kiln temperature model before researching the advanced control strategy. Because the tunnel kiln is a complicated object which is hard to establish an exact mathematic model, so this paper identifies the dynamic model of the tunnel kiln temperature object through BP neural network based on the data from field. After making the matlab simulative experiment, we get the conclusion that the effect of the tunnel kiln temperature model identified by BP neural network is good.
The tunnel kiln is a complicated object which has some characteristics, such as, hysteretic property, time-varying and non-linear, these charactertisics causing as to its temperature control more difficult. The conventional PID control technique can't get the perfect control effect because the three parameters of PID can't be set easily, the anti-interference and adapting ability of PID are week, and it depends on the operators' experience. In this paper, we take the tunnel kiln temperature model identified by neural network as an object to be controlled. According to the requirement of the temperature control, we design a kind of new controller based on fuzzy neural network. The controller has a distinct network structure. The learning ability of the neural network is used to revise the parameters of neural to enhance the learning ability of the fuzzy control. At last, making the matlab simulative experiment, we can make the conclusion that kiln temperature control system based on fuzzy neural network has better performance than the kiln temperature control system based on PID in the following aspects:better following performance for temperature varying, less adjusting time, greater anti-interference ability and greater robustness, so, the fuzzy neural network controller can meet the requirements of tunnel kiln temperature control.
引文
1.陈景雨.陶瓷工业热工过程及设备[M],中国轻工业出版社,1992,4.
2.高平良.陶瓷工业及陶瓷窑炉自动控制系统发展的展望[J],陶瓷,1995,(6)：21-23.
3.秦树凯,杨英华,陈晓波,张亚忠,郭小军,卜有康.泰国SRIC公司TK7高温隧道窑计算机控制系统[J],耐火材料,2007,41(3)：237-240.
4.肖丽.隧道窑监控系统设计及产品质量控制技术研究[D],沈阳：东北大学,2006.
5.雷涛,曾学军.现代隧道窑的计算机控制与管理系统[J],佛山陶瓷,2002,(12)：30-31.
6.李小雷.隧道窑两级微机控制系统的设计[J],中国建材装备,1996,(9)：245-249.
7.边冰.基于PLC硬件系统的隧道窑温度模糊控制方案[J],佛山陶瓷,2004,(9)：24-25.
8.陈文军.多功能Fuzzy/pid控制器的研究[D],武汉：武汉理工大学,2005.
9. Jiangjiang Wang, Dawei An, Chengzhi Lou. Application of Fuzzy-PID Controller in Heating Ventilating and Air-Conditioning System [J], IEEE,2006,6:2217-2218.
10.康赐荣.瓷器隧道窑燃烧过程的人工神经网络模糊控制[J],自动化与仪器仪表,2001,(3)：19-24.
11.宋彤,孙增国,冯冲.神经网络和模糊系统在故障诊断中的应用[J],计算技术与自动化,2004,23(2)：31-34.
12. DingChengJun, Zhang MingLu, DuanPing. Application of fuzzy neural networks in Information fusion for mobile robot [J], Control Theory and Application,2004, 21(1):59-62.
13. Jang S R. ANFIS:Adaptive-network-based fuzzy inference systems [J], IEEE Trans.on Systems, Man and Cybernetics,1993,23(3):665-685.
14. ZhangY Q. and Kandel A. Compensatory neuro fuzzy systems with fast learning algorithms [J], IEEE Trans.on Neural Networks,1998,9(1):83-105.
15. Zhang J. and Morris A.J. Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling [J], IEEE Trans.on Neural Networks,1999,10(2):313-326.
16. Lee K M, Kwak D H, Kwang H L. Fuzzy Inference Neural Network for Fuzzy Model Tuning [J], IEEE Trans.on Systems, Man and Cybernetics,1996,26(4): 637-645.
17.鲍鸿,黄心汉,李锡雄.广义模糊推理与广义模糊RBF神经网络[J],控制与决策,2000,15(2)：205-208.
18. Zhang ping, Wang you hua. A New Dynamic Decoupling Control Method Based on Fuzzy Neural network [C], Proceedings of the 25th Chinese Control Conference 7-11,2006,8:1102-1104.
19.孙增圻,张再兴,邓志东.智能控制理论与技术[M],北京：清华大学出版社,1997,170-180.
20. Chao C T, Chen Y J, Teng C C. Simplification of Fuzzy-Neural System Using Similarity Analysis [J], IEEE Trans.on Systems, Man and Cybernetics,1996,26(3): 344-354.
21.周志坚,毛宗源.一种最优模糊神经网络控制器[J],控制与决策,2000,15(3)：358-361.
22.罗钧,钱强.基于模糊神经网络的炉温控制系统研究[J],电子技术应用,2007,254(15)：155-157.
23. Xiaoyuan Wang, Liang Tian, Bing Chang. The Study of FNN Control on Direct Torque Control for BLDCM [C], Proceedings of the 6th World Congress on Intelligent Control and Automation,2006,6(21-23):3968-3972.
24.李少远等.Sugeno模糊模型的辨识与控制[J],自动化学报,1999,24(4)：488-492.
25. Sugeno M, Yasukawa T. A fuzzy logical based approach to qualitative modeling [J], IEEE Trans.on Fuzzy Sets,1993,1(1):7-25.
26. Wang L X, Mendel J M. Fuzzy basis functions, universal approximation and Orthogonal least squares learning [J], IEEE Trans.on Neural Networks,1992,3(5): 807-814.
27. Zhang Y Q. and Kandel A. Compensatory neuro fuzzy systems with fast learning algorithms [J]. IEEE Trans.on Neural Networks,1998,9(1):83-105.
28.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,2004,254-264.
29.张良杰,李衍达.模糊神经网络技术的新近发展[J],信息与控制,1995,24(1)：39-45.
30.陈建安.学习控制技术、方法和应用的发展新动向[J],控制理论与应用,1999,(2)：1-5.
31.陈铁军,胡宇博.基于改进GA的FNNC在水泥回转窑温度控制中的应用[J],计量与控制,2008,(4)：65-67.
32.王华强,陈文博,顾金晨.GA优化的模糊神经网络在炉温控制中的应用[J],合肥工业大学学报[J],2008,31(1)：60-62.
33.赵承滨.基于遗传算法的模糊神经控制研究[D],哈尔滨：哈尔滨理工大学,2004.
34.宋清昆,姜雷,郝敏.基于改进遗传算法的模糊神经网络控制[J],电机与控制 学报,2005,9(6)：617-620.
35.张其光,王执铨.基于遗传算法的模糊神经网络控制器设计及其稳定性分析[J],控制理论与控制工程,1999,16(5)：767-769.
36. Karlsruhe. PROFIBUS Trade Organization PTO.PROFIBUS System Description [M], Germany,2002,29-34.
37.童悦,汪建,郭旻.基于PLC的模糊控制在隧道通风中的应用[J],国内外机电一体化技术,2006,9(2)：37-40.
38.牛培峰,丁希生.两层模糊控制在循环流化床床温控制系统中的应用[J],燕山大学学报,2008,32(2)：124-128.
39.田秀华.模糊控制技术在恒压供水系统中的应用[J],信息技术,2008,(1),131-132.
40.王耀南.智能控制系统[M],湖南：湖南大学出版社,2006,252-304.
41.罗四维.大规模人工神经网络理论基础[M],北京：清华大学出版社,2004,1-9.
42.袁曾任.人工神经元网络及其应用[M],北京：清华大学出版社,1999,1-121.
43.高隽.人工神经网络原理及仿真实例[M],北京：机械工业出版社,2003,7-20.
44.康赐容.用人工神经网络实现模糊控制[J],电脑与信息技术,2001,(3)：11-12.
45.刘继梅,亓显玲,张明金.BP神经网络在带钢卷取温度控制中的应用[J],江西冶金,2008,28(1)：41-43.
46.段军彪,景旭,上官周平.基于遗传算法的BP神经在小流域侵蚀量预测中的应用[J],西北农业学报,2008,17(2)：317-320.
47.黄宝海,韩璞,葛忠真,何丽娜.基于现场数据的电站过热器神经网络建模[J],华北电力大学学报,2008,35(2)：66-69.
48.陈桦,程云艳.BP神经网络算法的改进及在MATLAB中的实现[J],陕西科技大学学报,2004,22(2)：45-47.
49.冯大勇.基于补偿算子的模糊神经网络系统及其在系统建模和控制中的应用研究[D],大连：大连理工大学,2000.
50.李国勇.智能控制及其MATLAB实现[M],北京：电子工业出版社,2005,5-75.
51.张新征.复杂间歇过程模型智能辨识方法研究[D],郑州：郑州大学,2004.
52. Yamaguchi Y. Self-Organizing Control Using Fuzzy Neural Networks [J], Int.J.Control,1992,56(2):415-419.
53.王笑颜,符雪桐.一种自学习模糊神经网络多变量自适应控制器[J],控制理论与应用,1999,16(2)：309-312.
54.龚菲,王永骥.神经网络自适应控制在电加热炉中的应用[J],工业加热,2002,(1)：5-7.
55. Samy Mesbah, Grantham Pang. Fuzzy-Neural Control with Application to a Heating System [C], Proceedings of the 1999 IEEE Intenrational Symposium on Computer Aided Control System Design. Kohala Coast-Island of Hawaii,1999, (8):22-27.
56. Romeo, Urbieta, Parrazales, Marco A. Ramirez, Osvaldo Espinosa S. Learning of a Backpropagation Neural Networks to Tune a Fuzzy Control of a Thermal System [J], IEEE Trans on Industry Application,2000,5(6):182-184.
57.袁佑新,卢立殊,王萍,王华斌.基于PID神经元的温度控制系统研究[J],武汉理工大学学报,2002,24(3)：21-23.
58. H.B.Hwarng, N.F.Hubele.X Control Chart Pattern Identification through Efficient off-line Neural Network Training [J], IEEE Transactions,1993,25(3):27-40.
59.李旭明.多变量模糊神经网络控制器的研究[J],控制与决策,2001,16(1)：107-110.
60. ZhaoYingkai, WangYiling. Intelligence Control of Nonlinear System Based on Matlab Simulation and the Real-time Control Platform [C], Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,786-789.
61.薛定宇.控制系统计算机辅助设计[M]：MATLAB语言与应用,北京：清华大学出版社,2006,183-231.
62.吕哲.隧道窑控制与管理系统设计及烧成带温度解耦控制研究[D],沈阳：东北大学,2006.
2.高平良.陶瓷工业及陶瓷窑炉自动控制系统发展的展望[J],陶瓷,1995,(6)：21-23.
3.秦树凯,杨英华,陈晓波,张亚忠,郭小军,卜有康.泰国SRIC公司TK7高温隧道窑计算机控制系统[J],耐火材料,2007,41(3)：237-240.
4.肖丽.隧道窑监控系统设计及产品质量控制技术研究[D],沈阳：东北大学,2006.
5.雷涛,曾学军.现代隧道窑的计算机控制与管理系统[J],佛山陶瓷,2002,(12)：30-31.
6.李小雷.隧道窑两级微机控制系统的设计[J],中国建材装备,1996,(9)：245-249.
7.边冰.基于PLC硬件系统的隧道窑温度模糊控制方案[J],佛山陶瓷,2004,(9)：24-25.
8.陈文军.多功能Fuzzy/pid控制器的研究[D],武汉：武汉理工大学,2005.
9. Jiangjiang Wang, Dawei An, Chengzhi Lou. Application of Fuzzy-PID Controller in Heating Ventilating and Air-Conditioning System [J], IEEE,2006,6:2217-2218.
10.康赐荣.瓷器隧道窑燃烧过程的人工神经网络模糊控制[J],自动化与仪器仪表,2001,(3)：19-24.
11.宋彤,孙增国,冯冲.神经网络和模糊系统在故障诊断中的应用[J],计算技术与自动化,2004,23(2)：31-34.
12. DingChengJun, Zhang MingLu, DuanPing. Application of fuzzy neural networks in Information fusion for mobile robot [J], Control Theory and Application,2004, 21(1):59-62.
13. Jang S R. ANFIS:Adaptive-network-based fuzzy inference systems [J], IEEE Trans.on Systems, Man and Cybernetics,1993,23(3):665-685.
14. ZhangY Q. and Kandel A. Compensatory neuro fuzzy systems with fast learning algorithms [J], IEEE Trans.on Neural Networks,1998,9(1):83-105.
15. Zhang J. and Morris A.J. Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling [J], IEEE Trans.on Neural Networks,1999,10(2):313-326.
16. Lee K M, Kwak D H, Kwang H L. Fuzzy Inference Neural Network for Fuzzy Model Tuning [J], IEEE Trans.on Systems, Man and Cybernetics,1996,26(4): 637-645.
17.鲍鸿,黄心汉,李锡雄.广义模糊推理与广义模糊RBF神经网络[J],控制与决策,2000,15(2)：205-208.
18. Zhang ping, Wang you hua. A New Dynamic Decoupling Control Method Based on Fuzzy Neural network [C], Proceedings of the 25th Chinese Control Conference 7-11,2006,8:1102-1104.
19.孙增圻,张再兴,邓志东.智能控制理论与技术[M],北京：清华大学出版社,1997,170-180.
20. Chao C T, Chen Y J, Teng C C. Simplification of Fuzzy-Neural System Using Similarity Analysis [J], IEEE Trans.on Systems, Man and Cybernetics,1996,26(3): 344-354.
21.周志坚,毛宗源.一种最优模糊神经网络控制器[J],控制与决策,2000,15(3)：358-361.
22.罗钧,钱强.基于模糊神经网络的炉温控制系统研究[J],电子技术应用,2007,254(15)：155-157.
23. Xiaoyuan Wang, Liang Tian, Bing Chang. The Study of FNN Control on Direct Torque Control for BLDCM [C], Proceedings of the 6th World Congress on Intelligent Control and Automation,2006,6(21-23):3968-3972.
24.李少远等.Sugeno模糊模型的辨识与控制[J],自动化学报,1999,24(4)：488-492.
25. Sugeno M, Yasukawa T. A fuzzy logical based approach to qualitative modeling [J], IEEE Trans.on Fuzzy Sets,1993,1(1):7-25.
26. Wang L X, Mendel J M. Fuzzy basis functions, universal approximation and Orthogonal least squares learning [J], IEEE Trans.on Neural Networks,1992,3(5): 807-814.
27. Zhang Y Q. and Kandel A. Compensatory neuro fuzzy systems with fast learning algorithms [J]. IEEE Trans.on Neural Networks,1998,9(1):83-105.
28.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,2004,254-264.
29.张良杰,李衍达.模糊神经网络技术的新近发展[J],信息与控制,1995,24(1)：39-45.
30.陈建安.学习控制技术、方法和应用的发展新动向[J],控制理论与应用,1999,(2)：1-5.
31.陈铁军,胡宇博.基于改进GA的FNNC在水泥回转窑温度控制中的应用[J],计量与控制,2008,(4)：65-67.
32.王华强,陈文博,顾金晨.GA优化的模糊神经网络在炉温控制中的应用[J],合肥工业大学学报[J],2008,31(1)：60-62.
33.赵承滨.基于遗传算法的模糊神经控制研究[D],哈尔滨：哈尔滨理工大学,2004.
34.宋清昆,姜雷,郝敏.基于改进遗传算法的模糊神经网络控制[J],电机与控制 学报,2005,9(6)：617-620.
35.张其光,王执铨.基于遗传算法的模糊神经网络控制器设计及其稳定性分析[J],控制理论与控制工程,1999,16(5)：767-769.
36. Karlsruhe. PROFIBUS Trade Organization PTO.PROFIBUS System Description [M], Germany,2002,29-34.
37.童悦,汪建,郭旻.基于PLC的模糊控制在隧道通风中的应用[J],国内外机电一体化技术,2006,9(2)：37-40.
38.牛培峰,丁希生.两层模糊控制在循环流化床床温控制系统中的应用[J],燕山大学学报,2008,32(2)：124-128.
39.田秀华.模糊控制技术在恒压供水系统中的应用[J],信息技术,2008,(1),131-132.
40.王耀南.智能控制系统[M],湖南：湖南大学出版社,2006,252-304.
41.罗四维.大规模人工神经网络理论基础[M],北京：清华大学出版社,2004,1-9.
42.袁曾任.人工神经元网络及其应用[M],北京：清华大学出版社,1999,1-121.
43.高隽.人工神经网络原理及仿真实例[M],北京：机械工业出版社,2003,7-20.
44.康赐容.用人工神经网络实现模糊控制[J],电脑与信息技术,2001,(3)：11-12.
45.刘继梅,亓显玲,张明金.BP神经网络在带钢卷取温度控制中的应用[J],江西冶金,2008,28(1)：41-43.
46.段军彪,景旭,上官周平.基于遗传算法的BP神经在小流域侵蚀量预测中的应用[J],西北农业学报,2008,17(2)：317-320.
47.黄宝海,韩璞,葛忠真,何丽娜.基于现场数据的电站过热器神经网络建模[J],华北电力大学学报,2008,35(2)：66-69.
48.陈桦,程云艳.BP神经网络算法的改进及在MATLAB中的实现[J],陕西科技大学学报,2004,22(2)：45-47.
49.冯大勇.基于补偿算子的模糊神经网络系统及其在系统建模和控制中的应用研究[D],大连：大连理工大学,2000.
50.李国勇.智能控制及其MATLAB实现[M],北京：电子工业出版社,2005,5-75.
51.张新征.复杂间歇过程模型智能辨识方法研究[D],郑州：郑州大学,2004.
52. Yamaguchi Y. Self-Organizing Control Using Fuzzy Neural Networks [J], Int.J.Control,1992,56(2):415-419.
53.王笑颜,符雪桐.一种自学习模糊神经网络多变量自适应控制器[J],控制理论与应用,1999,16(2)：309-312.
54.龚菲,王永骥.神经网络自适应控制在电加热炉中的应用[J],工业加热,2002,(1)：5-7.
55. Samy Mesbah, Grantham Pang. Fuzzy-Neural Control with Application to a Heating System [C], Proceedings of the 1999 IEEE Intenrational Symposium on Computer Aided Control System Design. Kohala Coast-Island of Hawaii,1999, (8):22-27.
56. Romeo, Urbieta, Parrazales, Marco A. Ramirez, Osvaldo Espinosa S. Learning of a Backpropagation Neural Networks to Tune a Fuzzy Control of a Thermal System [J], IEEE Trans on Industry Application,2000,5(6):182-184.
57.袁佑新,卢立殊,王萍,王华斌.基于PID神经元的温度控制系统研究[J],武汉理工大学学报,2002,24(3)：21-23.
58. H.B.Hwarng, N.F.Hubele.X Control Chart Pattern Identification through Efficient off-line Neural Network Training [J], IEEE Transactions,1993,25(3):27-40.
59.李旭明.多变量模糊神经网络控制器的研究[J],控制与决策,2001,16(1)：107-110.
60. ZhaoYingkai, WangYiling. Intelligence Control of Nonlinear System Based on Matlab Simulation and the Real-time Control Platform [C], Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,786-789.
61.薛定宇.控制系统计算机辅助设计[M]：MATLAB语言与应用,北京：清华大学出版社,2006,183-231.
62.吕哲.隧道窑控制与管理系统设计及烧成带温度解耦控制研究[D],沈阳：东北大学,2006.