冶金石灰窑多参数检测与质量控制理论及技术研究
|
摘要
钢铁工业是我国国民经济重要基础和支柱性产业，石灰是钢铁冶金的重要原料，它作为高炉炼铁和转炉炼钢的熔剂，具有缩短冶炼时间、提高生铁质量和钢水纯净度等优点，可广泛应用于湿法烟气脱硫、酸性工业废水处理等环境保护领域、以及轻质碳酸钙、环氧氯丙烷、氧化铝烧结矿制备等冶金、化工生产过程。石灰质量的主要衡量指标是活性度，活性石灰主要通过冶金石灰窑煅烧而成。石灰的活性度不仅决定了烟气脱硫和废水中和的效率、净化度，还决定着钢铁冶炼、轻质碳酸钙、氧化铝烧结矿等生产过程的产品质量、原料利用率和运行费用。随着钢铁工业的迅猛发展，对冶金石灰提出了越来越高的要求。因此，开展冶金石灰窑生产过程质量控制理论及技术的研究，实现整个工艺过程综合优化和安全稳定运行，对于进一步稳定石灰煅烧品质，降低生产能耗，减少环境污染、保证钢铁冶炼质量，提高经济效益都具有重要意义。
本文通过活性石灰煅烧涉及的物理和化学反应过程的机理研究，阐述了石灰窑生产系统的非线性、多变量和多参数影响的复杂特性以及石灰煅烧质量的有关影响因素，分析了活性度质量指标与煅烧时间、速度、温度和燃料配比等工艺参数之间的关系。在此基础上，以宝钢梅山钢铁公司套筒式石灰窑为对象，以其煤气流量检测、供热系统的温度调节和燃烧过程的优化控制为目标，采用现场技术参数测试、仿真分析、以及工业性试验相结合的方法进行石灰窑生产多参数检测与质量控制理论及技术的研究，以达到实现整个生产过程综合优化和安全稳定运行的目的。主要研究内容及创新点如下：
（1）运用参数补偿原理设计并实现混合煤气流量在线检测方案
工程上通常采用差压式孔板流量计进行冶金石灰窑的混合煤气流量在线检测，本文通过其误差产生因素的分析，提出了以超声波流量计为基准检测总管煤气流量进而实现对各支管煤气流量在线补偿的方法，推导了石灰窑混合煤气流量检测的在线密度补偿算法，进而制定了多参数补偿的混合煤气流量计量检测方案，并进一步构建了混合煤气流量在线补偿检测系统，在此基础上，通过硬件配置、选型和软件设计、开发以及现场调试阐述了整个系统的实现。
（2）基于信息融合技术的燃烧过程烟气氧含量的软测量研究
本文针对石灰窑最佳燃烧控制系统的重要衡量参数—尾气氧含量的在线检测问题，采用基于信息融合技术的软测量方法开展研究。提出了尾气氧含量的机理建模方案，根据机理表达式详细分析了相关参数影响因素，并推导出氧含量的计算修正公式，在此基础上，构建了基于信息融合技术的氧含量软测量系统的分析计算模型。
（3）采用模糊控制策略设计燃烧过程温度调节系统
针对石灰窑燃烧过程温度控制所具有的有非线性、大滞后、强耦合等特点，采用模糊控制策略，构建石灰窑温度调节系统模糊控制方案，并针对其存在的问题，进一步提出了模糊PID控制方法。通过Matlab的仿真分析，验证了模糊PID控制策略能获得较好的动态控制性能。
（4）基于经济运行的冶金石灰窑燃烧过程优化控制
本文通过石灰窑经济燃烧的特性分析，提出了提高热效率，实时调整最佳空燃比系数的方法，并制定了完善的燃烧尾气含氧量闭环控制方案，从而保证了当供热燃气混合比发生变化而导致燃料热值波动时，能有效地达到对过剩空气量的最佳控制，实现石灰窑生产过程的经济运行。论文还采用BP网络建立了石灰窑燃烧系统的优化控制模型，并将遗传算法应用于神经网络的连接权值和阈值优化计算，提高了BP神经网络的计算精度和收敛速度，从而达到石灰窑生产过程综合优化和稳定运行的效果。
Steel industry is an important basis for China's national economy and pillar industry. Lime is animportant raw material of steel metallurgy. As a blast furnace and converter steelmaking flux, it canreduce refining time and improve the quality of pig iron and steel purity, etc. And it can be widelyused in the field of environmental protection such as wet flue gas desulphurization and acidicindustrial wastewater treatment field of environmental protection. It is also used in the metallurgicaland chemical production processes such as calcium carbonate, epichlorohydrin, and preparation ofalumina sintering. The main measure of the quality of lime is the degree of activity. The active limeis mainly calcite by metallurgical lime kiln. The activity of lime not only determines the efficiencyand purification degree of flue gas desulfurization and waste water neutralization, also determinesthe product quality, raw material utilization and operating costs of iron and steel smelting, lightcalcium carbonate and alumina sinter and other production processes. With the rapid development ofsteel industry, we need increasing demands on metallurgical lime. Therefore, we need to carry out aresearch on the control theory and technology of metallurgical lime kiln production process quality.Then the whole process can be integrated optimized and be work securely and steadily. This is ofgreat significance on further stabilizing the calcite lime quality, reduce energy consumption, reduceenvironmental pollution, ensure the quality of the steel smelting and improve the economicefficiency.
With the research on the physical and chemical mechanism of the process of active limecalcinations, this paper describes the nonlinear multi-variable and multi-parameter productionsystem of the lime-kiln complex nature and the influencing factors of the lime calcimine quality, andanalysis the relationship between the degree of activity indicators of active lime and calcimine time,speed, temperature, fuel ratio and other parameters. On this basis, Baosteel Meishan Iron and SteelCompany sleeve lime will be the targeted, Its gas flow detection, temperature regulation of heatingsystem and optimize control of combustion process will be the object, We are going to use themethod of combining the on-site technical parameters test, simulation analysis and industrial test toget a research on multi-parameter detection and quality control theory and technology of limeproduction so that the entire production process may be comprehensive optimization and worksecurely and steadily. The main contents and innovations are as follows:
（1）Use the principle of compensation with the parameter to the design and implement the online detection program of mixed-gas flow.
In engineering, we usually use differential pressure orifice flow meter to achieve an on-linedetection on mixed gas flow of metallurgical lime. This paper analyzes the causes of the errors andthe ultrasonic flow meter is proposed as the fiducially flow meter to detect the main gas flow so as to give an on-line detection on each branch pipe. Then it derives the density on-line compensationalgorithm of the mixed gas flow detection, develop a multi-parameter compensated mixed gas flowmeasurement detection scheme and further build the mixed gas flow detection system of on-linecompensation. On this basis, through the hardware configuration, selections, software design,development and site commissioning, this paper elaborate how the entire system is achieve.
（2）Aresearch on the soft measurement of oxygen content of the combustion process based onthe information fusion technology.
In this paper, as the on-line detection on oxygen content in the exhaust gas of the bestcombustion control system for the lime kiln, the soft measurement based on the information fusiontechnology is used to launch a research. We propose the mechanism modeling scheme of exhaustoxygen content. According to the expression of the mechanism, we detailed analysis the factors ofthe relevant parameters and give the modified formula to calculate the content of oxygen. And wederived the correction formula calculation of oxygen content. On this basis, constructed analysismodel of oxygen soft sensor system based on information fusion.
（3）Use the fuzzy control strategy to design a combustion temperature control system.
As for the nonlinear, large delay and strong coupling of the lime kiln temperature control for thecombustion process, we use the fuzzy control strategy to build a fuzzy control program for the limekiln temperature control system and further put forward the fuzzy PID control method to solve theexisting problems. Simulation and analysis by MATLAB, we verify the fuzzy PID control strategycan achieve a better dynamic control performance.
（4）The metallurgy lime kiln control of combustion process optimization based on theeconomic operation.
In this paper, through the analysis of the characteristics of the lime kiln economic burning, weput forward a method of improving the thermal efficiency and real-time adjusting the best air-fuelratio coefficient. Then a comprehensive closed-loop control program of detecting the oxygen contentin the combustion exhaust gas is developed. As a result, when the calorific value of fuel fluctuatesbecause of the changes of heating gas mixing ratio, the amount of excess air can still be optimalcontrol effectively so that the lime kiln production process will be work economically. The paperalso established an optimal control model of lime kiln combustion system with the BP neuralnetwork. Using the genetic algorithm to calculate the connection weights and threshold withoptimization, the accuracy and convergence rate of BP neural network have been improved. As aresult, we achieve the lime kiln production process integration and optimization and work steadily.
本文通过活性石灰煅烧涉及的物理和化学反应过程的机理研究，阐述了石灰窑生产系统的非线性、多变量和多参数影响的复杂特性以及石灰煅烧质量的有关影响因素，分析了活性度质量指标与煅烧时间、速度、温度和燃料配比等工艺参数之间的关系。在此基础上，以宝钢梅山钢铁公司套筒式石灰窑为对象，以其煤气流量检测、供热系统的温度调节和燃烧过程的优化控制为目标，采用现场技术参数测试、仿真分析、以及工业性试验相结合的方法进行石灰窑生产多参数检测与质量控制理论及技术的研究，以达到实现整个生产过程综合优化和安全稳定运行的目的。主要研究内容及创新点如下：
（1）运用参数补偿原理设计并实现混合煤气流量在线检测方案
工程上通常采用差压式孔板流量计进行冶金石灰窑的混合煤气流量在线检测，本文通过其误差产生因素的分析，提出了以超声波流量计为基准检测总管煤气流量进而实现对各支管煤气流量在线补偿的方法，推导了石灰窑混合煤气流量检测的在线密度补偿算法，进而制定了多参数补偿的混合煤气流量计量检测方案，并进一步构建了混合煤气流量在线补偿检测系统，在此基础上，通过硬件配置、选型和软件设计、开发以及现场调试阐述了整个系统的实现。
（2）基于信息融合技术的燃烧过程烟气氧含量的软测量研究
本文针对石灰窑最佳燃烧控制系统的重要衡量参数—尾气氧含量的在线检测问题，采用基于信息融合技术的软测量方法开展研究。提出了尾气氧含量的机理建模方案，根据机理表达式详细分析了相关参数影响因素，并推导出氧含量的计算修正公式，在此基础上，构建了基于信息融合技术的氧含量软测量系统的分析计算模型。
（3）采用模糊控制策略设计燃烧过程温度调节系统
针对石灰窑燃烧过程温度控制所具有的有非线性、大滞后、强耦合等特点，采用模糊控制策略，构建石灰窑温度调节系统模糊控制方案，并针对其存在的问题，进一步提出了模糊PID控制方法。通过Matlab的仿真分析，验证了模糊PID控制策略能获得较好的动态控制性能。
（4）基于经济运行的冶金石灰窑燃烧过程优化控制
本文通过石灰窑经济燃烧的特性分析，提出了提高热效率，实时调整最佳空燃比系数的方法，并制定了完善的燃烧尾气含氧量闭环控制方案，从而保证了当供热燃气混合比发生变化而导致燃料热值波动时，能有效地达到对过剩空气量的最佳控制，实现石灰窑生产过程的经济运行。论文还采用BP网络建立了石灰窑燃烧系统的优化控制模型，并将遗传算法应用于神经网络的连接权值和阈值优化计算，提高了BP神经网络的计算精度和收敛速度，从而达到石灰窑生产过程综合优化和稳定运行的效果。
Steel industry is an important basis for China's national economy and pillar industry. Lime is animportant raw material of steel metallurgy. As a blast furnace and converter steelmaking flux, it canreduce refining time and improve the quality of pig iron and steel purity, etc. And it can be widelyused in the field of environmental protection such as wet flue gas desulphurization and acidicindustrial wastewater treatment field of environmental protection. It is also used in the metallurgicaland chemical production processes such as calcium carbonate, epichlorohydrin, and preparation ofalumina sintering. The main measure of the quality of lime is the degree of activity. The active limeis mainly calcite by metallurgical lime kiln. The activity of lime not only determines the efficiencyand purification degree of flue gas desulfurization and waste water neutralization, also determinesthe product quality, raw material utilization and operating costs of iron and steel smelting, lightcalcium carbonate and alumina sinter and other production processes. With the rapid development ofsteel industry, we need increasing demands on metallurgical lime. Therefore, we need to carry out aresearch on the control theory and technology of metallurgical lime kiln production process quality.Then the whole process can be integrated optimized and be work securely and steadily. This is ofgreat significance on further stabilizing the calcite lime quality, reduce energy consumption, reduceenvironmental pollution, ensure the quality of the steel smelting and improve the economicefficiency.
With the research on the physical and chemical mechanism of the process of active limecalcinations, this paper describes the nonlinear multi-variable and multi-parameter productionsystem of the lime-kiln complex nature and the influencing factors of the lime calcimine quality, andanalysis the relationship between the degree of activity indicators of active lime and calcimine time,speed, temperature, fuel ratio and other parameters. On this basis, Baosteel Meishan Iron and SteelCompany sleeve lime will be the targeted, Its gas flow detection, temperature regulation of heatingsystem and optimize control of combustion process will be the object, We are going to use themethod of combining the on-site technical parameters test, simulation analysis and industrial test toget a research on multi-parameter detection and quality control theory and technology of limeproduction so that the entire production process may be comprehensive optimization and worksecurely and steadily. The main contents and innovations are as follows:
（1）Use the principle of compensation with the parameter to the design and implement the online detection program of mixed-gas flow.
In engineering, we usually use differential pressure orifice flow meter to achieve an on-linedetection on mixed gas flow of metallurgical lime. This paper analyzes the causes of the errors andthe ultrasonic flow meter is proposed as the fiducially flow meter to detect the main gas flow so as to give an on-line detection on each branch pipe. Then it derives the density on-line compensationalgorithm of the mixed gas flow detection, develop a multi-parameter compensated mixed gas flowmeasurement detection scheme and further build the mixed gas flow detection system of on-linecompensation. On this basis, through the hardware configuration, selections, software design,development and site commissioning, this paper elaborate how the entire system is achieve.
（2）Aresearch on the soft measurement of oxygen content of the combustion process based onthe information fusion technology.
In this paper, as the on-line detection on oxygen content in the exhaust gas of the bestcombustion control system for the lime kiln, the soft measurement based on the information fusiontechnology is used to launch a research. We propose the mechanism modeling scheme of exhaustoxygen content. According to the expression of the mechanism, we detailed analysis the factors ofthe relevant parameters and give the modified formula to calculate the content of oxygen. And wederived the correction formula calculation of oxygen content. On this basis, constructed analysismodel of oxygen soft sensor system based on information fusion.
（3）Use the fuzzy control strategy to design a combustion temperature control system.
As for the nonlinear, large delay and strong coupling of the lime kiln temperature control for thecombustion process, we use the fuzzy control strategy to build a fuzzy control program for the limekiln temperature control system and further put forward the fuzzy PID control method to solve theexisting problems. Simulation and analysis by MATLAB, we verify the fuzzy PID control strategycan achieve a better dynamic control performance.
（4）The metallurgy lime kiln control of combustion process optimization based on theeconomic operation.
In this paper, through the analysis of the characteristics of the lime kiln economic burning, weput forward a method of improving the thermal efficiency and real-time adjusting the best air-fuelratio coefficient. Then a comprehensive closed-loop control program of detecting the oxygen contentin the combustion exhaust gas is developed. As a result, when the calorific value of fuel fluctuatesbecause of the changes of heating gas mixing ratio, the amount of excess air can still be optimalcontrol effectively so that the lime kiln production process will be work economically. The paperalso established an optimal control model of lime kiln combustion system with the BP neuralnetwork. Using the genetic algorithm to calculate the connection weights and threshold withoptimization, the accuracy and convergence rate of BP neural network have been improved. As aresult, we achieve the lime kiln production process integration and optimization and work steadily.
引文
[1]邓新云，颜鑫，卢云峰.活性石灰生产工艺过程条件的优化[J].2008,18(2):22-26
[2]刘青川.浅谈活性石灰对炼钢的影响[J].一重技术，2006，(1):26-27
[3]陈子琦.活性石灰竖窑窑型比选[J].炼钢,1999,15(5):52-54
[4]唐亚新.影响石灰活性的因素分析[J].炼钢,2001,17(3):50-52,62
[5]宋延辉，张羡夫.活性石灰的生产及在冶金中的应用[J].河北理工学院学报,2006,28(1):5-11
[6]苗辛，赵志宇.冶金竖窑石灰质量的控制途径[J].冶金标准化与质量,2008,46(1):32-34,37
[7] Dasgupta S S. Vertical Shaft Lime Kiln[J]. Iron&Steel Review,2007,51(3):128-129,132
[8]毕衍涛，赵培建，安源.浅谈济钢新建石灰窑窑型的确定[J].山东冶金，2005,25(supplement):11-13
[9]李喜太，梅瑛．套筒式气烧石灰窑[J]．山西化工，1999,(3)：27-30．
[10]邹琳江，邹得球,陈江林.套筒石灰窑的发展概况[J].工业炉,2007,29(2):12-14,24
[11]李道忠,费心举.气烧套筒窑生产活性石灰的理论与实践[J].中国冶金,2004(7):23-26
[12]任子奎.套筒式竖窑在济钢的应用[J].冶金信息导刊,2005(2):24-26
[13]罗琨.套筒式竖窑工艺特点及其相关工艺配置.武钢技术,2002,40(1):51-55
[14]邹琳江,闫东峰,涂叔颖.气烧套筒石灰竖窑生产活性石灰的工艺特点[J].工业炉,2009,31(1):13-15
[15] ANDREJ SENEGACNIK,JANEZ OMAN,BRANE SIROK. Annular shaft kiln for limeburning with kiln gas recirculation[J].Applied Thermal Engineering,2008,28(7):785-792
[16]涂叔颖,闫东峰.气烧套筒石灰竖窑活性石灰生产过程[J].能源技术,2009,31(1):61-63
[17] HONGHI TRAN. Lime Kiln Chemistry[C].2002Kraft Recovery Short Course, Jan7-10,2002.Orlando, Florida
[18]梁国伟,蔡武昌.流量测量技术及仪表[M].北京：机械工业出版社,2005
[19] CHENG MING-TAO, FU JING-QI. A Differential Pressure Mass Flowmeter Design Based onIndustrial Ethernet Interface[C].7th International Measurement TechnologySeminar(6156-6159),August5,2007. Beijing.
[20]樊军庆张宝珍.温度控制理论的民展概况[J].学术探讨,2008,(5):40-41
[21]刘金琨.先进PID控制及其MATLAB仿真[M].北京：电子工业出版社，2003
[22]何克忠，李伟.计算机控制系统[M].北京：清华大学出版社，2007
[23] ANDREJ SENEGACNIK JANEZ OMAN BRANE SIROK. Annular shaft kiln for limeburning with kiln gas recirculation[J].Applied Thermal Engineering,2008,28(7):785-792
[24] TERRY N.ADAMS. Lime Kiln Principles and Operations[C]. TAPPI Kraft Recovery Course2007, Jan1,2007. St. Petersburg, Florida, USA
[25]张海鹏，路军平，周芳德.气体流量测量时的密度补偿[J].计量技术,2005,(1):31-32
[26] PROCESSING GROUP. Ultrasonic flowmeter market to reach590million by2012[J].Processing,2008,21(10):8
[27] WILLIAM WRIGHT, SALEM BRINI. Capacitive ultrasonic transducers for gas flow meteringapplications[C].13th International Flow Measurement Conference(Jan.1,2005),Peebles,Scotland
[28] YEH T T, ESPINA P I. An Intelligent Ultrasonic Flow Meter for Improved Flow Measurementand Flow Calibration Facility[C].IEEE Instrumentation and measurement TechnologyConference, May21-23,2001. Budapest, Hungary
[29]崔建霞.超声波流量计的选型及应用[J].矿山机械，2006,34(4):90-92
[30]董志勇.浅谈超声波流量计的基本原理及类型[J].计量与测试技术，2008,35(10):8-10
[31]程维兰.时差式超声波流量计的应用[J].技术创新，2009,(2):105-106
[32] DENG CHAO-FENG, J M DIAS PEREIRA. The application of ultrasonic sensors in smartflowmeters[C]. The9th World Multi-conference on Systemics, Cybernetics and Informatics,Jan1,2005. Orlando, Florida, USA
[33]斯小峰.超声波流量计与孔板流量计的比较[J].自动化博览,2006,(8)：68-70
[34]张红光.流量计量中密度补偿的研究[D].四川：四川大学，2003
[35]万云，居琪萍，文方.超声波流量计在煤气流量检测方面的应用[J].江西冶金,2004,24(4):23-27
[36]刘阳.模糊控制在加热窑温度控制中的研究及应用[D].北京：华中科技大学，2004
[37]章晋武董德智洪乃刚.套筒式石灰窑温度的模糊控制策略和分析[J].自动化技术与应用，2006，25(5):24-26
[38]李伟、董继先、汤伟等.自整定PID控制器在白泥回转窑中的应用[J].轻工机械，2009,27(3):33-35
[39]韩泰伦.最新石灰生产工艺技术管理及防污染措施操作实务全书.吉林电子出版社.2004年
[40]章晋武,董德智,洪乃刚.套筒式石灰窑温度的模糊控制策略和分析[J].自动化技术与应用,2006(5):48～51.
[41]高全.本钢套筒窑下拱桥坍塌原因分析[J],耐火与石灰,2008(2):22～23.
[42][意]特鲁兹·弗卡斯公司技术部.TF石灰窑的设计和调试原则[J].石灰,2008(1):37～38.
[43]王显科.机械化石灰竖窑的自动检测控制系统[J].鞍钢技术,2002(1):55～56.
[44]马学军,李辉燕,李相波,杨云.大型石灰立窑高精度温度控制技术研究[J].黄石理工学院学报.2007(3):4～8.
[45]王显科.机械化石灰竖窑的自动检测控制系统[J].鞍钢技术,2002(1):48～49.
[46]阮学斌,郑宁,肖师荣,陈云龙,蔡宝辉.三明钢铁厂气烧石灰窑计算机控制系统[J].冶金自动化,2000(4):86～87.
[47]邸敏艳,姚勇,刘恒,李金鑫.PROFIBUS-DP技术在石灰窑电气控制系统改造中的应用[J],工业仪表与自动化装置.2008(5):58~59.
[48]徐旭常.燃烧理论与燃烧设备[M].北京：机械工业出版社，1988
[49]曹社占.舞钢石灰窑结瘤原因分析及其控制系统研究[J].北京科技大学学报,2007(4):42～44。
[50]常金龙.竖炉石灰窑数值模拟研究[J].内蒙古科技大学,2007(5):30～33.
[51]许波,李临,陈志彬.石灰竖窑PLC控制系统[J],计算机应用,2005(1):62～63.
[52]吴晓蜂,郑锡才.PLC在活性石灰竖窑控制系统中的应用[J].冶金自动化,2007(3):31～34.
[53]许星,唐其伟,罗世亮,程良伦.基于S-300PLC的石灰窑炉控制系统[J].工业控制计算机,2004(12):67～68.
[54]廖志锋.基于PLC的石灰竖窑自动控制系统[J].冶金丛刊,2008(4):31～32.
[55]荆增良．模糊控制技术与应用选编[M]．北京：北京航空航天丈学出版社,1999.
[56]黄青,陆福辉,黄瑞坚.石灰竖窑工序控制点运行实践[J].柳钢科技,2003(1):51～53.
[57]任燕玲,任启亮,朱高峰.陈明英热值燃烧PID控制在MaerzPFR石灰窑中的优化[J].计算机与数字工程。2009(6):37~39.
[58]王永初，任秀珍.工业过程控制系统设计范例[M].北京：科学出版社，1986
[59] Chen G. Conventionat and fuzzy PID controllers An overview．1nt J． Intelligent Control＆System s，1996(1)：235~246
[60] Jang J S R．Sun C T. Neuro-fuzzy modeling and contro1．In：Proc. of IEEE．1995(3)：378~406
[61] Zadeh L A．Fuzzy sets．Information and Control，1965(8)：338~353.
[62] Xingsheng Deng, Xinzhou Wang. Incremental learning of dynamic fuzzy neural networks foraccurate system modeling[J]. Fuzzy Sets and Systems,2009,160(7):972-987.
[63] Gang Wang, Huaguang Zhang, Bing Chen, Shaocheng Tong. Fuzzy hyperbolic neural networkwith time-varying delays[J]. Fuzzy Sets and Systems,2010,161(19):2533-2551.
[64] Martin těpni ka, Ond ej Polakovi. A neural network approach to the fuzzy transform[J].Fuzzy Sets and Systems,2009,160(8):1037-1047.
[65] Min Han, Yannan Sun, Yingnan Fan. An improved fuzzy neural network based on T–S model[J].Expert Systems with Applications,2008,38(4):2905-2920.
[66]黄向华.控制系统仿真[M].北京:北京航空航天大学出版社,2008.
[67]张晓,戴冠中,徐乃平.遗传算法在抽取和过滤模糊控制规则中的应用研究.控制理论与应用,1998,15(3):736-744.
[68] Y. H. Joo, H. S. Hwang. Fuzzy Systems Modeling by Fuzzy Partition and GA Hybird Schemes.Fuzzy Sets and Systems[J],1997,86(2):279-288
[69] S. G. Cao, N. W. Rees, G. Feng. Analysis and Design for a Class of Complex Control Systems.Part II:Fuzzy Modeling and Identification[J]. Automatica,1997,33(6):1029-1039
[70] Ching-Chang Wong, Nine-Shen Lin. Rule Extraction for Fuzzy Modeling. Fuzzy Sets andSystems[J],1997,88(1):23-30
[71] Euntal Kim, Minkee Park, Seunghwan Ji. A New Approach to Fuzzy Modeling[J]. IEEE Trans.on Fuzzy Systems,1997,5(3):337-342
[72] Jin-Shing Roger Jang, Chuen-Tshi Sun. Neuro-Fuzzy Modeling and Control[J]. Proc ofIEEE,1995,83(3):378-404
[73] Yih-Guang Leu, Tsu-Tian Lee, Wei-Yen Wang. On-Line Tuning of Fuzzy-Neural Network forAdapative Control of Nonlinear Dynamical Systems[J]. IEEE Trans. on Systems, Man andCybernetics,1997,27(6):1034-1043
[74] Min Yao.A Kind of Adaptive Fuzzy System[J].Control Theory and Application,1998,15(3):364-371.
[75] S. B. Chen, L. Wu. Self-Learning Fuzzy Neural Networks for Control of Uncertain Systemswith Time Delays[J]. IEEE Trans.on Systems, Man and Cybernetcs,1997,27(2):142-148.
[76]李旭明.多变量模糊神经网络控制器的研究.控制与决策,2001,16(1):107-110。
[77]诸静.模糊控制原理与应用.北京:机械工业出版社,1999.
[78]李洪兴,王群.工程模糊数学方法及其应用.天津:科学技术出版社,1993.
[79] Nan Jiang, Zhiye Zhao, Liqun Ren. Design of structural modular neural networks with geneticalgorithm[J]. Advances in Engineering Software.2003,34(1):17-24.
[80] Randall S. Sexton, Jatinder N. D. Gupta. Comparative evaluation of genetic algorithm andbackpropagation for training neural networks[J]. Information Sciences.2000,129(1):45-59.
[81] D Whitley, T Starkweather, C Bogart.Genetic algorithms and neural networks: optimizingconnections and connectivity[J]. Parallel Computing.1990,14(3):347-361.
[82] J. Yen, L. Wang. Simplification of Fuzzy Rule Based Systems Using OrthogonalTransformation[J].Proc.6th IEEE. Conf. Fuzzy Systems, Barcelona, Spain,1997.
[83] C. T. Sun, Rule-Based Structure Identification in an Adaptive-Network-Based Fuzzy InferenceSystem[J]. IEEE Trans. on Fuzzy Systems,1994,2(1):64-73.
[84] H. R. Berenji. An Introduction to Fuzzy Logic Applications in Intelligent Systems[J]. New York,Kluwer Academic Publishers,1992:69-96
[85] Xuyang Lou, Baotong Cui. Robust asymptotic stability of uncertain fuzzy BAM neuralnetworks with time-varying delays[J]. Fuzzy Sets and Systems.2007,158(24):2746-2756.
[86] Shian-Tang Tzeng. Design of fuzzy wavelet neural networks using the GA approach forfunction approximation and system identification[J]. Fuzzy Sets andSystems.2010,161(19):2585-2596.
[87] Gang Wang, Huaguang Zhang, Bing Chen, Shaocheng Tong. Fuzzy hyperbolic neural networkwith time-varying delays[J]. Fuzzy Sets and Systems.2010,161(19):2585-2596.
[88] Hsu-Kun Wu, Jer-Guang Hsieh, Yih-Lon Lin, Jyh-Horng Jeng. On maximum likelihood fuzzyneural networks[J]. Fuzzy Sets and Systems.2010,161(21):2795-2807.
[89] Min Han, Yannan Sun, Yingnan Fan. An improved fuzzy neural network based on T–S model[J].Expert Systems with Applications.2008,34(4):2905-2920.
[90] Ching-Hung Lee, Yu-Ching Lin. An adaptive neuro-fuzzy filter design via periodic fuzzy neuralnetwork[J]. Signal Processing.2005,85(2):401-411.
[91] M. Reza Mashinchi, Ali Selamat. An improvement on genetic-based learning method for fuzzyartificial neural networks[J]. Applied Soft Computing.2009,9(4):1208-1216.
[92] C.J. Mantas, J.M. Puche, J.M. Mantas. Extraction of similarity based fuzzy rules from artificialneural networks[J]. International Journal of Approximate Reasoning.2006,43(2):202-221.
[93]金晓明,荣冈.基于李雅普诺夫稳定性的模糊关系模糊辨识算法.控制理论与应用,1997,14(2):228-232
[94]孙圣何,赵保军.模型参考模糊神经网络控制器的开发.控制与决策,1996,11(4):34-39.
[95] J.Moody. Fast Learning in Networks of Locally-Timed Processing Units. NeuralComputation,1989,13(1):281-294.
[96] S.Renals. Classification Experiments Using Radial Basic Functions. Proc.Int. Joint Conferenceon Neural Network,1989,Paris:461-467.
[97] R.M.Sanner. Gaussian Networks for Direct Adaptive Control. Proc. American ControlConference,1991,Istenbul:2153-2159.
[98] G.Cybenko. Approximation by Superposition of Sigmoidal Function. Mathematical of Control,Signal and System,1989,2:303-314.
[99] Jeen Lin, Ruey-Jing Lian. Hybrid fuzzy-logic and neural-network controller for MIMOsystems[J]. Mechatronics.2009,19(6):972-986.
[100] A. Ciaramella, R. Tagliaferri, W. Pedrycz, A. Di Nola. Fuzzy relational neural network[J].International Journal of Approximate Reasoning.2006,41(2):146-163.
[101] Chi-Huang Lu, Ching-Chih Tsai. Generalized predictive control using recurrent fuzzy neuralnetworks for industrial processes[J]. Journal of Process Control.2007,17(1):83-92.
[102] Anas Quteishat, Chee Peng Lim. A modified fuzzy min–max neural network with ruleextraction and its application to fault detection and classification[J]. Applied SoftComputing.2008,8(2):985-995.
[103] Richard Thompson, Arthur Dexter. A fuzzy decision-making approach to temperature controlin air-conditioning systems[J].Control Engineering Practice.2005,13(6):689-698.
[104] Ming He, Wen-Jian Cai, Shao-Yuan Li. Multiple fuzzy model-based temperature predictivecontrol for HVAC systems[J]. Information Sciences.2005,169(2):155-174.
[105] M. Santos, A. L. Dexter.Control of a cryogenic process using a fuzzy PID scheduler[J].Control Engineering Practice.2002,10(10):1147-1152.
[106] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control[J]. Fuzzy Sets andSystems.2004,143(2):311-333.
[107] Kenichiro Hayashi, Akifumi Otsubo, etc. Realization of nonlinear and linear PID controlsusing simplified indirect fuzzy inference method[J]. Fuzzy Sets andSystems.1999,105(3):409-414.
[108] Hyun-Joon Cho, Kwang-Bo Cho, Bo-Hyeun Wang. Fuzzy-PID hybrid control: Automatic rulegeneration using genetic algorithms[J]. Fuzzy Sets and Systems.1997,92(3):305-316.
[109] Jian-ming Zheng, Sheng-dun Zhao, Shu-guo Wei. Application of self-tuning fuzzy PIDcontroller for a SRM direct drive volume control hydraulic press[J]. Control EngineeringPractice.2009,17(12):1398-1404.
[110] Le Hung Lan. Stability analysis for a class of Takagi–Sugeno fuzzy control systems with PIDcontrollers[J]. International Journal of Approximate Reasoning.2007,46(1):109-119.
[111] A. Fadaei, K. Salahshoor. Design and implementation of a new fuzzy PID controller fornetworked control systems[J]. ISA Transactions.2008,47(4):351-361.
[112] Minyou Chen, D. A. Linkens. A hybrid neuro-fuzzy PID controller[J]. Fuzzy Sets andSystems.1998,99(1):27-36.
[113] Leonid Reznik, Omar Ghanayem, Anna Bourmistrov. PID plus fuzzy controller structures as adesign base for industrial applications[J]. Engineering Applications of ArtificialIntelligence.2000,13(4):419-430.
[114] T. P. Blanchett, G. C. Kember, R. Dubay. PID gain scheduling using fuzzy logic[J]. ISATransactions.2000,39(3):317-325.
[115] James Carvajal, Guanrong Chen, Haluk Ogmen. Fuzzy PID controller: Design, performanceevaluation, and stability analysis[J]. Information Sciences.2000,123(3):249-270.
[116] K.K. Ahn, D.Q. Truong. Online tuning fuzzy PID controller using robust extended Kalmanfilter[J]. Journal of Process Control.2009,19(6):1011-1023.
[117] Dragan D. Kukolj, Slobodan B. Kuzmanovi, Emil Levi. Design of a PID-like compound fuzzylogic controller[J]. Engineering Applications of Artificial Intelligence.2001,14(6):785-803.
[118] B.M. Mohan, Arpita Sinha. Analytical structure and stability analysis of a fuzzy PIDcontroller[J]. Applied Soft Computing.2008,8(1):749-758.
[119] Ching-Hung Lee, Ching-Cheng Teng.Calculation of PID controller parameters by using afuzzy neural network[J]. ISA Transactions.2003,42(3):391-400.
[120] Yau-Tarng Juang, Yun-Tien Chang, Chih-Peng Huang.Design of fuzzy PID controllers usingmodified triangular membership functions[J]. Information Sciences.2008,78(5):1325-1333.
[121]柴天佑.生产制造全流程优化控制对控制与优化理论方法的挑战.自动化学报，2009,6:646-648
[122] Wu Hong-Xin, Wang Ying-Chun, Xing Yan. Intelligent charcteristic model based intelligentcontrol and its applications. Science in China (Series E),2002,32(6):805816
[2]刘青川.浅谈活性石灰对炼钢的影响[J].一重技术，2006，(1):26-27
[3]陈子琦.活性石灰竖窑窑型比选[J].炼钢,1999,15(5):52-54
[4]唐亚新.影响石灰活性的因素分析[J].炼钢,2001,17(3):50-52,62
[5]宋延辉，张羡夫.活性石灰的生产及在冶金中的应用[J].河北理工学院学报,2006,28(1):5-11
[6]苗辛，赵志宇.冶金竖窑石灰质量的控制途径[J].冶金标准化与质量,2008,46(1):32-34,37
[7] Dasgupta S S. Vertical Shaft Lime Kiln[J]. Iron&Steel Review,2007,51(3):128-129,132
[8]毕衍涛，赵培建，安源.浅谈济钢新建石灰窑窑型的确定[J].山东冶金，2005,25(supplement):11-13
[9]李喜太，梅瑛．套筒式气烧石灰窑[J]．山西化工，1999,(3)：27-30．
[10]邹琳江，邹得球,陈江林.套筒石灰窑的发展概况[J].工业炉,2007,29(2):12-14,24
[11]李道忠,费心举.气烧套筒窑生产活性石灰的理论与实践[J].中国冶金,2004(7):23-26
[12]任子奎.套筒式竖窑在济钢的应用[J].冶金信息导刊,2005(2):24-26
[13]罗琨.套筒式竖窑工艺特点及其相关工艺配置.武钢技术,2002,40(1):51-55
[14]邹琳江,闫东峰,涂叔颖.气烧套筒石灰竖窑生产活性石灰的工艺特点[J].工业炉,2009,31(1):13-15
[15] ANDREJ SENEGACNIK,JANEZ OMAN,BRANE SIROK. Annular shaft kiln for limeburning with kiln gas recirculation[J].Applied Thermal Engineering,2008,28(7):785-792
[16]涂叔颖,闫东峰.气烧套筒石灰竖窑活性石灰生产过程[J].能源技术,2009,31(1):61-63
[17] HONGHI TRAN. Lime Kiln Chemistry[C].2002Kraft Recovery Short Course, Jan7-10,2002.Orlando, Florida
[18]梁国伟,蔡武昌.流量测量技术及仪表[M].北京：机械工业出版社,2005
[19] CHENG MING-TAO, FU JING-QI. A Differential Pressure Mass Flowmeter Design Based onIndustrial Ethernet Interface[C].7th International Measurement TechnologySeminar(6156-6159),August5,2007. Beijing.
[20]樊军庆张宝珍.温度控制理论的民展概况[J].学术探讨,2008,(5):40-41
[21]刘金琨.先进PID控制及其MATLAB仿真[M].北京：电子工业出版社，2003
[22]何克忠，李伟.计算机控制系统[M].北京：清华大学出版社，2007
[23] ANDREJ SENEGACNIK JANEZ OMAN BRANE SIROK. Annular shaft kiln for limeburning with kiln gas recirculation[J].Applied Thermal Engineering,2008,28(7):785-792
[24] TERRY N.ADAMS. Lime Kiln Principles and Operations[C]. TAPPI Kraft Recovery Course2007, Jan1,2007. St. Petersburg, Florida, USA
[25]张海鹏，路军平，周芳德.气体流量测量时的密度补偿[J].计量技术,2005,(1):31-32
[26] PROCESSING GROUP. Ultrasonic flowmeter market to reach590million by2012[J].Processing,2008,21(10):8
[27] WILLIAM WRIGHT, SALEM BRINI. Capacitive ultrasonic transducers for gas flow meteringapplications[C].13th International Flow Measurement Conference(Jan.1,2005),Peebles,Scotland
[28] YEH T T, ESPINA P I. An Intelligent Ultrasonic Flow Meter for Improved Flow Measurementand Flow Calibration Facility[C].IEEE Instrumentation and measurement TechnologyConference, May21-23,2001. Budapest, Hungary
[29]崔建霞.超声波流量计的选型及应用[J].矿山机械，2006,34(4):90-92
[30]董志勇.浅谈超声波流量计的基本原理及类型[J].计量与测试技术，2008,35(10):8-10
[31]程维兰.时差式超声波流量计的应用[J].技术创新，2009,(2):105-106
[32] DENG CHAO-FENG, J M DIAS PEREIRA. The application of ultrasonic sensors in smartflowmeters[C]. The9th World Multi-conference on Systemics, Cybernetics and Informatics,Jan1,2005. Orlando, Florida, USA
[33]斯小峰.超声波流量计与孔板流量计的比较[J].自动化博览,2006,(8)：68-70
[34]张红光.流量计量中密度补偿的研究[D].四川：四川大学，2003
[35]万云，居琪萍，文方.超声波流量计在煤气流量检测方面的应用[J].江西冶金,2004,24(4):23-27
[36]刘阳.模糊控制在加热窑温度控制中的研究及应用[D].北京：华中科技大学，2004
[37]章晋武董德智洪乃刚.套筒式石灰窑温度的模糊控制策略和分析[J].自动化技术与应用，2006，25(5):24-26
[38]李伟、董继先、汤伟等.自整定PID控制器在白泥回转窑中的应用[J].轻工机械，2009,27(3):33-35
[39]韩泰伦.最新石灰生产工艺技术管理及防污染措施操作实务全书.吉林电子出版社.2004年
[40]章晋武,董德智,洪乃刚.套筒式石灰窑温度的模糊控制策略和分析[J].自动化技术与应用,2006(5):48～51.
[41]高全.本钢套筒窑下拱桥坍塌原因分析[J],耐火与石灰,2008(2):22～23.
[42][意]特鲁兹·弗卡斯公司技术部.TF石灰窑的设计和调试原则[J].石灰,2008(1):37～38.
[43]王显科.机械化石灰竖窑的自动检测控制系统[J].鞍钢技术,2002(1):55～56.
[44]马学军,李辉燕,李相波,杨云.大型石灰立窑高精度温度控制技术研究[J].黄石理工学院学报.2007(3):4～8.
[45]王显科.机械化石灰竖窑的自动检测控制系统[J].鞍钢技术,2002(1):48～49.
[46]阮学斌,郑宁,肖师荣,陈云龙,蔡宝辉.三明钢铁厂气烧石灰窑计算机控制系统[J].冶金自动化,2000(4):86～87.
[47]邸敏艳,姚勇,刘恒,李金鑫.PROFIBUS-DP技术在石灰窑电气控制系统改造中的应用[J],工业仪表与自动化装置.2008(5):58~59.
[48]徐旭常.燃烧理论与燃烧设备[M].北京：机械工业出版社，1988
[49]曹社占.舞钢石灰窑结瘤原因分析及其控制系统研究[J].北京科技大学学报,2007(4):42～44。
[50]常金龙.竖炉石灰窑数值模拟研究[J].内蒙古科技大学,2007(5):30～33.
[51]许波,李临,陈志彬.石灰竖窑PLC控制系统[J],计算机应用,2005(1):62～63.
[52]吴晓蜂,郑锡才.PLC在活性石灰竖窑控制系统中的应用[J].冶金自动化,2007(3):31～34.
[53]许星,唐其伟,罗世亮,程良伦.基于S-300PLC的石灰窑炉控制系统[J].工业控制计算机,2004(12):67～68.
[54]廖志锋.基于PLC的石灰竖窑自动控制系统[J].冶金丛刊,2008(4):31～32.
[55]荆增良．模糊控制技术与应用选编[M]．北京：北京航空航天丈学出版社,1999.
[56]黄青,陆福辉,黄瑞坚.石灰竖窑工序控制点运行实践[J].柳钢科技,2003(1):51～53.
[57]任燕玲,任启亮,朱高峰.陈明英热值燃烧PID控制在MaerzPFR石灰窑中的优化[J].计算机与数字工程。2009(6):37~39.
[58]王永初，任秀珍.工业过程控制系统设计范例[M].北京：科学出版社，1986
[59] Chen G. Conventionat and fuzzy PID controllers An overview．1nt J． Intelligent Control＆System s，1996(1)：235~246
[60] Jang J S R．Sun C T. Neuro-fuzzy modeling and contro1．In：Proc. of IEEE．1995(3)：378~406
[61] Zadeh L A．Fuzzy sets．Information and Control，1965(8)：338~353.
[62] Xingsheng Deng, Xinzhou Wang. Incremental learning of dynamic fuzzy neural networks foraccurate system modeling[J]. Fuzzy Sets and Systems,2009,160(7):972-987.
[63] Gang Wang, Huaguang Zhang, Bing Chen, Shaocheng Tong. Fuzzy hyperbolic neural networkwith time-varying delays[J]. Fuzzy Sets and Systems,2010,161(19):2533-2551.
[64] Martin těpni ka, Ond ej Polakovi. A neural network approach to the fuzzy transform[J].Fuzzy Sets and Systems,2009,160(8):1037-1047.
[65] Min Han, Yannan Sun, Yingnan Fan. An improved fuzzy neural network based on T–S model[J].Expert Systems with Applications,2008,38(4):2905-2920.
[66]黄向华.控制系统仿真[M].北京:北京航空航天大学出版社,2008.
[67]张晓,戴冠中,徐乃平.遗传算法在抽取和过滤模糊控制规则中的应用研究.控制理论与应用,1998,15(3):736-744.
[68] Y. H. Joo, H. S. Hwang. Fuzzy Systems Modeling by Fuzzy Partition and GA Hybird Schemes.Fuzzy Sets and Systems[J],1997,86(2):279-288
[69] S. G. Cao, N. W. Rees, G. Feng. Analysis and Design for a Class of Complex Control Systems.Part II:Fuzzy Modeling and Identification[J]. Automatica,1997,33(6):1029-1039
[70] Ching-Chang Wong, Nine-Shen Lin. Rule Extraction for Fuzzy Modeling. Fuzzy Sets andSystems[J],1997,88(1):23-30
[71] Euntal Kim, Minkee Park, Seunghwan Ji. A New Approach to Fuzzy Modeling[J]. IEEE Trans.on Fuzzy Systems,1997,5(3):337-342
[72] Jin-Shing Roger Jang, Chuen-Tshi Sun. Neuro-Fuzzy Modeling and Control[J]. Proc ofIEEE,1995,83(3):378-404
[73] Yih-Guang Leu, Tsu-Tian Lee, Wei-Yen Wang. On-Line Tuning of Fuzzy-Neural Network forAdapative Control of Nonlinear Dynamical Systems[J]. IEEE Trans. on Systems, Man andCybernetics,1997,27(6):1034-1043
[74] Min Yao.A Kind of Adaptive Fuzzy System[J].Control Theory and Application,1998,15(3):364-371.
[75] S. B. Chen, L. Wu. Self-Learning Fuzzy Neural Networks for Control of Uncertain Systemswith Time Delays[J]. IEEE Trans.on Systems, Man and Cybernetcs,1997,27(2):142-148.
[76]李旭明.多变量模糊神经网络控制器的研究.控制与决策,2001,16(1):107-110。
[77]诸静.模糊控制原理与应用.北京:机械工业出版社,1999.
[78]李洪兴,王群.工程模糊数学方法及其应用.天津:科学技术出版社,1993.
[79] Nan Jiang, Zhiye Zhao, Liqun Ren. Design of structural modular neural networks with geneticalgorithm[J]. Advances in Engineering Software.2003,34(1):17-24.
[80] Randall S. Sexton, Jatinder N. D. Gupta. Comparative evaluation of genetic algorithm andbackpropagation for training neural networks[J]. Information Sciences.2000,129(1):45-59.
[81] D Whitley, T Starkweather, C Bogart.Genetic algorithms and neural networks: optimizingconnections and connectivity[J]. Parallel Computing.1990,14(3):347-361.
[82] J. Yen, L. Wang. Simplification of Fuzzy Rule Based Systems Using OrthogonalTransformation[J].Proc.6th IEEE. Conf. Fuzzy Systems, Barcelona, Spain,1997.
[83] C. T. Sun, Rule-Based Structure Identification in an Adaptive-Network-Based Fuzzy InferenceSystem[J]. IEEE Trans. on Fuzzy Systems,1994,2(1):64-73.
[84] H. R. Berenji. An Introduction to Fuzzy Logic Applications in Intelligent Systems[J]. New York,Kluwer Academic Publishers,1992:69-96
[85] Xuyang Lou, Baotong Cui. Robust asymptotic stability of uncertain fuzzy BAM neuralnetworks with time-varying delays[J]. Fuzzy Sets and Systems.2007,158(24):2746-2756.
[86] Shian-Tang Tzeng. Design of fuzzy wavelet neural networks using the GA approach forfunction approximation and system identification[J]. Fuzzy Sets andSystems.2010,161(19):2585-2596.
[87] Gang Wang, Huaguang Zhang, Bing Chen, Shaocheng Tong. Fuzzy hyperbolic neural networkwith time-varying delays[J]. Fuzzy Sets and Systems.2010,161(19):2585-2596.
[88] Hsu-Kun Wu, Jer-Guang Hsieh, Yih-Lon Lin, Jyh-Horng Jeng. On maximum likelihood fuzzyneural networks[J]. Fuzzy Sets and Systems.2010,161(21):2795-2807.
[89] Min Han, Yannan Sun, Yingnan Fan. An improved fuzzy neural network based on T–S model[J].Expert Systems with Applications.2008,34(4):2905-2920.
[90] Ching-Hung Lee, Yu-Ching Lin. An adaptive neuro-fuzzy filter design via periodic fuzzy neuralnetwork[J]. Signal Processing.2005,85(2):401-411.
[91] M. Reza Mashinchi, Ali Selamat. An improvement on genetic-based learning method for fuzzyartificial neural networks[J]. Applied Soft Computing.2009,9(4):1208-1216.
[92] C.J. Mantas, J.M. Puche, J.M. Mantas. Extraction of similarity based fuzzy rules from artificialneural networks[J]. International Journal of Approximate Reasoning.2006,43(2):202-221.
[93]金晓明,荣冈.基于李雅普诺夫稳定性的模糊关系模糊辨识算法.控制理论与应用,1997,14(2):228-232
[94]孙圣何,赵保军.模型参考模糊神经网络控制器的开发.控制与决策,1996,11(4):34-39.
[95] J.Moody. Fast Learning in Networks of Locally-Timed Processing Units. NeuralComputation,1989,13(1):281-294.
[96] S.Renals. Classification Experiments Using Radial Basic Functions. Proc.Int. Joint Conferenceon Neural Network,1989,Paris:461-467.
[97] R.M.Sanner. Gaussian Networks for Direct Adaptive Control. Proc. American ControlConference,1991,Istenbul:2153-2159.
[98] G.Cybenko. Approximation by Superposition of Sigmoidal Function. Mathematical of Control,Signal and System,1989,2:303-314.
[99] Jeen Lin, Ruey-Jing Lian. Hybrid fuzzy-logic and neural-network controller for MIMOsystems[J]. Mechatronics.2009,19(6):972-986.
[100] A. Ciaramella, R. Tagliaferri, W. Pedrycz, A. Di Nola. Fuzzy relational neural network[J].International Journal of Approximate Reasoning.2006,41(2):146-163.
[101] Chi-Huang Lu, Ching-Chih Tsai. Generalized predictive control using recurrent fuzzy neuralnetworks for industrial processes[J]. Journal of Process Control.2007,17(1):83-92.
[102] Anas Quteishat, Chee Peng Lim. A modified fuzzy min–max neural network with ruleextraction and its application to fault detection and classification[J]. Applied SoftComputing.2008,8(2):985-995.
[103] Richard Thompson, Arthur Dexter. A fuzzy decision-making approach to temperature controlin air-conditioning systems[J].Control Engineering Practice.2005,13(6):689-698.
[104] Ming He, Wen-Jian Cai, Shao-Yuan Li. Multiple fuzzy model-based temperature predictivecontrol for HVAC systems[J]. Information Sciences.2005,169(2):155-174.
[105] M. Santos, A. L. Dexter.Control of a cryogenic process using a fuzzy PID scheduler[J].Control Engineering Practice.2002,10(10):1147-1152.
[106] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control[J]. Fuzzy Sets andSystems.2004,143(2):311-333.
[107] Kenichiro Hayashi, Akifumi Otsubo, etc. Realization of nonlinear and linear PID controlsusing simplified indirect fuzzy inference method[J]. Fuzzy Sets andSystems.1999,105(3):409-414.
[108] Hyun-Joon Cho, Kwang-Bo Cho, Bo-Hyeun Wang. Fuzzy-PID hybrid control: Automatic rulegeneration using genetic algorithms[J]. Fuzzy Sets and Systems.1997,92(3):305-316.
[109] Jian-ming Zheng, Sheng-dun Zhao, Shu-guo Wei. Application of self-tuning fuzzy PIDcontroller for a SRM direct drive volume control hydraulic press[J]. Control EngineeringPractice.2009,17(12):1398-1404.
[110] Le Hung Lan. Stability analysis for a class of Takagi–Sugeno fuzzy control systems with PIDcontrollers[J]. International Journal of Approximate Reasoning.2007,46(1):109-119.
[111] A. Fadaei, K. Salahshoor. Design and implementation of a new fuzzy PID controller fornetworked control systems[J]. ISA Transactions.2008,47(4):351-361.
[112] Minyou Chen, D. A. Linkens. A hybrid neuro-fuzzy PID controller[J]. Fuzzy Sets andSystems.1998,99(1):27-36.
[113] Leonid Reznik, Omar Ghanayem, Anna Bourmistrov. PID plus fuzzy controller structures as adesign base for industrial applications[J]. Engineering Applications of ArtificialIntelligence.2000,13(4):419-430.
[114] T. P. Blanchett, G. C. Kember, R. Dubay. PID gain scheduling using fuzzy logic[J]. ISATransactions.2000,39(3):317-325.
[115] James Carvajal, Guanrong Chen, Haluk Ogmen. Fuzzy PID controller: Design, performanceevaluation, and stability analysis[J]. Information Sciences.2000,123(3):249-270.
[116] K.K. Ahn, D.Q. Truong. Online tuning fuzzy PID controller using robust extended Kalmanfilter[J]. Journal of Process Control.2009,19(6):1011-1023.
[117] Dragan D. Kukolj, Slobodan B. Kuzmanovi, Emil Levi. Design of a PID-like compound fuzzylogic controller[J]. Engineering Applications of Artificial Intelligence.2001,14(6):785-803.
[118] B.M. Mohan, Arpita Sinha. Analytical structure and stability analysis of a fuzzy PIDcontroller[J]. Applied Soft Computing.2008,8(1):749-758.
[119] Ching-Hung Lee, Ching-Cheng Teng.Calculation of PID controller parameters by using afuzzy neural network[J]. ISA Transactions.2003,42(3):391-400.
[120] Yau-Tarng Juang, Yun-Tien Chang, Chih-Peng Huang.Design of fuzzy PID controllers usingmodified triangular membership functions[J]. Information Sciences.2008,78(5):1325-1333.
[121]柴天佑.生产制造全流程优化控制对控制与优化理论方法的挑战.自动化学报，2009,6:646-648
[122] Wu Hong-Xin, Wang Ying-Chun, Xing Yan. Intelligent charcteristic model based intelligentcontrol and its applications. Science in China (Series E),2002,32(6):805816