基于组分快速检测方法的型砂质量直接优化控制技术研究
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摘要
型砂质量控制一直是铸造领域的重要研究课题。由于缺乏有效粘土含量的快速检测方法,目前的湿型粘土砂质量控制系统多采用由性能检测到组分控制的间接控制模式。本文在型砂组分快速检测方法的基础上,研究了一种湿型粘土砂质量直接控制技术。研究内容主要包括湿型粘土砂导电特性与有效粘土含量和含水量关系的试验研究,型砂主要组分快速检测方法测试电路参数的确定,求解型砂组分的人工神经元网络模型的构建,湿型砂质量分布式控制系统的研制。研究结果表明,基于组分快速检测方法的由组分测试到组分控制的直接控制模式可以实现型砂质量优化控制。
通过试验研究与理论分析,证明了湿型粘土砂的导电特性与其主要成分——有效粘土含量和含水量有确定的关系。提出了砂样导电的RC串联等效模型,并定性分析了电极形状、外加激励源的幅值、频率、型砂组分等因素与砂样的等效电阻Rs、等效电容C_d的非线性关系。砂样电极采用嵌入样筒内壁的弧形电极,交流激励源为频率1kHz、峰值10V的正弦波,直流激励源的电压幅值为+9V。研究了湿型砂紧实率与有效粘土含量、含水量及水土比的关系,并分析了回用砂中死粘土对紧实率的影响。
研制了由自动制样机构和测试下位机构成的湿型砂组分及紧实率快速测试单元,可完成标准砂样的制作和紧实率、导电参数的自动测量。自动制样机构采用压缩空气驱动,压力设定值为4.5×10~5pa,在制样过程中同步进行实砂压力及压头位移的测量,由测试下位机自动计算出砂样紧实率。紧实率的测量范围为:10～60%±1%。测试单元可在10秒钟时间内与上位机配合完成有效粘土含量及含水量的快速求解。
构建了型砂有效粘土含量和含水量的人工神经元网络求解模型。该网络为三层结构的BP网络,输入节点为4个,分别是湿型粘土砂交流导电能力、初始直流导电能力、直流导电能力变化率和紧实率;输出节点为2个,分别是型砂的有效粘土含量和含水量;网络包含一个隐含层,隐含层节点为6个。为了克服原材料来源不稳定对求解精度产生的影响,对BP算法进行了改进以提高网络模型的泛化性能和鲁棒性。有效粘土含量的求解精度为±0.3%,含水量的求解精度为±0.2%。
提出了基于组分快速在线测试方法的数字PID加料控制算法,对混砂机的加料量进行控制。根据混砂试验的经验数据建立了模糊规则表,采用模糊式参数自整定方法对PID参数进行在线自整定。研制了混砂机加料控制单元,包括自动加料机构和加料控制下位机,可实现手动、自动加料及闭环联网加料控制。自动加料机构由螺旋给料器、称料单元、加水单元等部分组成。加料控制下位机完成加料控制算法的运算,控制加料机构动作完成加料过程。构建了由两级计算机组成的分布式控制系统。上位机完成现场测试数据的采集、实时显示、存储及分析,人工神经元网络运算以及系统各项参数的设置。下位机控制机构动作,完成标准砂样制作、信息参数采集与上传以及型砂组分的定量添加。计算机系统具有在线故障自动诊断与处理功能,对系统关键部件的运行状态进行实时监测、分级管理,使系统的稳定性、实用性达到铸造车间生产实际的要求。试验运行结果表明,本文研制的型砂质量直接优化控制系统的型砂组分控制误差分别为:有效粘土含量<±0.5%,含水量<±0.3%。
The quality control of green sand has been the important task in domain of foundry all the while. Because there is no fast testing method for active clay, most of the green sand quality control systems at present work in direct mode, from performances testing to ingredient controlling. Based on a fast ingredient testing method, a direct control technique of green sand quality is investigated in this dissertation. The research includes experimentally studying of the relationship between conductive capabilities of bentonite-bonded molding sand and its active clay and water content, selecting the parameters of ingredient testing circuit, modeling of the artificial neural network for green sand ingredient calculation, developing the distributed control system for green sand quality. The investigation results have proved that the optimization control of green sand quality can be realized in direct mode, from ingredient testing to ingredient controlling, based on fast ingredient testing method.
The relations between conductive capabilities of bentonite-bonded molding sand and its main ingredient, active clay and moisture, are confirmed by experimental research and theoretical analysis. The equivalent model of series RC for sand specimen conductivity is put forward, the nonlinear relationships among the model, equivalent resistor Rs and equivalent capacity Cd, and various factors such as electrode shape, amplitude and frequency of external exciting power source and green sand ingredient are analyzed qualitatively. A pair of electrodes in arc-shaped are embedded in inner wall of specimen tube, the frequency and peak value of AC exciting power source with sine wave are 1kHz and 10V respectively, the amplitude of DC exciting power source is +9V. The relations among green sand compactability, active clay, moisture and ratio of water to clay are studied, and the influence of dead clay on compactability in returned sand is also analyzed.
The green sand ingredient and compactability fast testing unit, made up of an automatic sampling machine and a testing slave, is developed to prepare a standard sand specimen and test conductive parameters automatically. The automatic sampling machine works under the drive of compressed air and the working pressure is 4.5×105pa. During the process, the compacting pressure and displacement of compressing head are measured simultaneously and compactability of green sand is calculated by the testing slave. The testing range of compactability is 10~60%±1%. Cooperating with host computer, the testing unit can display and print the active clay and moisture of green sand within 10 seconds.
An artificial neural network model to solve the active clay and moisture of green sand is designed. The model is a three-layer BP network with 4 input nodes and 2 output nodes. The input nodes represent green sand AC conductivity, original DC conductivity, variety rate of DC conductivity and compactability. The output nodes represent green sand active clay and moisture. The network includes a hidden layer with 6 nodes. In order to overcome the influence of instability of moulding materials on precision of ingredient calculation, some innovations of BP algorithm are taken to improve the generalization and robustness of BP network. The precision of solving active clay is±0.3%, and the moisture is±0.2%.
Based on the fast ingredient testing method, a digital PID feeding control algorithm is put forward to control the feeding operation of the sand muller. Fuzzy rules are set up by expirical data from mixing experiment, and PID parameters are adjusted by fuzzy self-tuning method. A feeding control unit, including an automatic feeding machine and a feeding control slave, is developed and it is able to feed ingredients in manual, automatic and closed-loop modes. The automatic feeding machine consists of screw feeders, weighing filler and water adding unit. The feeding control slave is used to run the PID algorithm, and control the operation of feeding machine.
A distributed control system including two-level computers is constructed. The host computer is used to collect and display field measuring data in real time, save and analyze them, run artificial neural network program and set all parameters of the system. The tasks of slave computers are controlling sampling machine, collecting and uploading information parameters and feeding green sand ingredients quantificationally. The system has the function of automatic on-line trouble shooting, monitoring the operation states of system key parts in real time and supervising them in grade, insuring the stability and practicability of system to meet the requirement of real production in foundry workshop. Experimental running result indicates that the control error of green sand ingredients with optimizing direct control system developed in this thesis are active clay<±0.5%, and moisture content<±0.3%.
通过试验研究与理论分析,证明了湿型粘土砂的导电特性与其主要成分——有效粘土含量和含水量有确定的关系。提出了砂样导电的RC串联等效模型,并定性分析了电极形状、外加激励源的幅值、频率、型砂组分等因素与砂样的等效电阻Rs、等效电容C_d的非线性关系。砂样电极采用嵌入样筒内壁的弧形电极,交流激励源为频率1kHz、峰值10V的正弦波,直流激励源的电压幅值为+9V。研究了湿型砂紧实率与有效粘土含量、含水量及水土比的关系,并分析了回用砂中死粘土对紧实率的影响。
研制了由自动制样机构和测试下位机构成的湿型砂组分及紧实率快速测试单元,可完成标准砂样的制作和紧实率、导电参数的自动测量。自动制样机构采用压缩空气驱动,压力设定值为4.5×10~5pa,在制样过程中同步进行实砂压力及压头位移的测量,由测试下位机自动计算出砂样紧实率。紧实率的测量范围为:10～60%±1%。测试单元可在10秒钟时间内与上位机配合完成有效粘土含量及含水量的快速求解。
构建了型砂有效粘土含量和含水量的人工神经元网络求解模型。该网络为三层结构的BP网络,输入节点为4个,分别是湿型粘土砂交流导电能力、初始直流导电能力、直流导电能力变化率和紧实率;输出节点为2个,分别是型砂的有效粘土含量和含水量;网络包含一个隐含层,隐含层节点为6个。为了克服原材料来源不稳定对求解精度产生的影响,对BP算法进行了改进以提高网络模型的泛化性能和鲁棒性。有效粘土含量的求解精度为±0.3%,含水量的求解精度为±0.2%。
提出了基于组分快速在线测试方法的数字PID加料控制算法,对混砂机的加料量进行控制。根据混砂试验的经验数据建立了模糊规则表,采用模糊式参数自整定方法对PID参数进行在线自整定。研制了混砂机加料控制单元,包括自动加料机构和加料控制下位机,可实现手动、自动加料及闭环联网加料控制。自动加料机构由螺旋给料器、称料单元、加水单元等部分组成。加料控制下位机完成加料控制算法的运算,控制加料机构动作完成加料过程。构建了由两级计算机组成的分布式控制系统。上位机完成现场测试数据的采集、实时显示、存储及分析,人工神经元网络运算以及系统各项参数的设置。下位机控制机构动作,完成标准砂样制作、信息参数采集与上传以及型砂组分的定量添加。计算机系统具有在线故障自动诊断与处理功能,对系统关键部件的运行状态进行实时监测、分级管理,使系统的稳定性、实用性达到铸造车间生产实际的要求。试验运行结果表明,本文研制的型砂质量直接优化控制系统的型砂组分控制误差分别为:有效粘土含量<±0.5%,含水量<±0.3%。
The quality control of green sand has been the important task in domain of foundry all the while. Because there is no fast testing method for active clay, most of the green sand quality control systems at present work in direct mode, from performances testing to ingredient controlling. Based on a fast ingredient testing method, a direct control technique of green sand quality is investigated in this dissertation. The research includes experimentally studying of the relationship between conductive capabilities of bentonite-bonded molding sand and its active clay and water content, selecting the parameters of ingredient testing circuit, modeling of the artificial neural network for green sand ingredient calculation, developing the distributed control system for green sand quality. The investigation results have proved that the optimization control of green sand quality can be realized in direct mode, from ingredient testing to ingredient controlling, based on fast ingredient testing method.
The relations between conductive capabilities of bentonite-bonded molding sand and its main ingredient, active clay and moisture, are confirmed by experimental research and theoretical analysis. The equivalent model of series RC for sand specimen conductivity is put forward, the nonlinear relationships among the model, equivalent resistor Rs and equivalent capacity Cd, and various factors such as electrode shape, amplitude and frequency of external exciting power source and green sand ingredient are analyzed qualitatively. A pair of electrodes in arc-shaped are embedded in inner wall of specimen tube, the frequency and peak value of AC exciting power source with sine wave are 1kHz and 10V respectively, the amplitude of DC exciting power source is +9V. The relations among green sand compactability, active clay, moisture and ratio of water to clay are studied, and the influence of dead clay on compactability in returned sand is also analyzed.
The green sand ingredient and compactability fast testing unit, made up of an automatic sampling machine and a testing slave, is developed to prepare a standard sand specimen and test conductive parameters automatically. The automatic sampling machine works under the drive of compressed air and the working pressure is 4.5×105pa. During the process, the compacting pressure and displacement of compressing head are measured simultaneously and compactability of green sand is calculated by the testing slave. The testing range of compactability is 10~60%±1%. Cooperating with host computer, the testing unit can display and print the active clay and moisture of green sand within 10 seconds.
An artificial neural network model to solve the active clay and moisture of green sand is designed. The model is a three-layer BP network with 4 input nodes and 2 output nodes. The input nodes represent green sand AC conductivity, original DC conductivity, variety rate of DC conductivity and compactability. The output nodes represent green sand active clay and moisture. The network includes a hidden layer with 6 nodes. In order to overcome the influence of instability of moulding materials on precision of ingredient calculation, some innovations of BP algorithm are taken to improve the generalization and robustness of BP network. The precision of solving active clay is±0.3%, and the moisture is±0.2%.
Based on the fast ingredient testing method, a digital PID feeding control algorithm is put forward to control the feeding operation of the sand muller. Fuzzy rules are set up by expirical data from mixing experiment, and PID parameters are adjusted by fuzzy self-tuning method. A feeding control unit, including an automatic feeding machine and a feeding control slave, is developed and it is able to feed ingredients in manual, automatic and closed-loop modes. The automatic feeding machine consists of screw feeders, weighing filler and water adding unit. The feeding control slave is used to run the PID algorithm, and control the operation of feeding machine.
A distributed control system including two-level computers is constructed. The host computer is used to collect and display field measuring data in real time, save and analyze them, run artificial neural network program and set all parameters of the system. The tasks of slave computers are controlling sampling machine, collecting and uploading information parameters and feeding green sand ingredients quantificationally. The system has the function of automatic on-line trouble shooting, monitoring the operation states of system key parts in real time and supervising them in grade, insuring the stability and practicability of system to meet the requirement of real production in foundry workshop. Experimental running result indicates that the control error of green sand ingredients with optimizing direct control system developed in this thesis are active clay<±0.5%, and moisture content<±0.3%.
引文
1 G. J.Vingas. Sand Control of Green Sand by a Simple Inventory Method. AFS Trans, 1970(78): 213-216
2 Joyce Sara, Ziegler Mark, LaFay Vic. Optimizing New Sand and Core Sand Additions in Foundry Green Sand Systems. Foundry Management & Technology, 2005, 133(8): 46-59
3 Kvasha F.S. Influence of Sand Preparation System Factors on Consumption of Moulding Materials. Soviet Castings Technology (English Translation of Liteinoe Proizvodstvo), 1987(2): 60-64
4 H. G. Levelink. Steuerung des Sandqualittat in modernen Formanlagen. Giesserei, 1975, 62(5)
5 Lavington M. H. Sand Preparation-A Guide to Current Concepts in Mulling and Mixing. Foundry Trade Journal, 1983, 154(3260): 433-447
6 Foundry M&T staff. Key Fundamentals for Sand Preparation and Reclamation. Foundry Management & Technology, 1989, 117(4)
7 Rad Nader S., Tumay Mehmet T. Factors Affecting Sand Specimen Preparation by Raining. Geotechnical Testing Journal, 1987, 10(1): 31-37
8 Granlund M.J.. Understanding the Basics of Green Sand Testing. Modern Casting, 1999, 89(3): 38
9 袁永健. 混砂机配料称量及型砂水分在线自动检测装置. 中国铸造装备与技术, 2000 (5):48-50
10 T. Takemura. Theory to Estimate the Stress due to Moisture in Wood Using a Transmission-type Microwave Moisture Meter. Journal of Wood Science. 2003, 49(1): 11-17
11 王强, 杨占奎, 石晶玉等. 型砂水分红外光谱在线检测方法的探讨. 中国铸造装备与技术, 2003(6): 21-22
12 Pickrell K.C., Sergeant G.F., Wolfe M. Tips for Auditing Your Green Sand System. Modern Casting, 1999, 89(3): 30
13 Sindermann H. Green Sand Compaction by Impulse and Dynamic Molding. Transactions of the American Foundrymen's Society, 1995(102): 321
14 Bonacic-Mandinic Z., Budic I., Solenicki G. The Influence of Molding Sand on
the Compressive Stresses. Metalurgija, 2004, 43(1): 17-22
15 Kuerbis R., Vaid Y.P. Sand Sample Preparation - the Slurry Deposition Method. Soils and Foundations, 1988, 28(4): 107-118
16 Heine Hans J. Innovative Techniques Spur Green Molding Sand Preparation. Foundry Management & Technology, 1987, 115(8): 20-25
17 Li Dayong. Study on Testing Available Clay and Moisture Content of Green Sand Rapidly With New Method. 4th Asian Foundry Congress Proceedings (Australia), 1996,10
18 李大勇, 李庆春等. 基于双电源二次激励法的湿型砂组分快速预测用网络模型的研究. 电机与控制学报, 1999(9): 16-19
19 李剑中, 陆杰. 型砂紧实率快速测试方法的研究与实现. 制造业自动化, 2000, 22(5): 54-55
20 姚继成, 隋世城, 于海岩等. 型砂紧实率在线控制装置的研究及应用. 铸造, 2005, 54(9): 909-911
21 Schulze W.E. Green sand system controls. Modern Casting, 1990, 80(6): 68-69
22 T. Hanai, et al. Influence of Preparation Method of Test Specimen on Green Sand Measurements. Proceedings of the 65thWorld Foundry Congress, Gyeongju, Korea, 2002: 859-867
23 Alagarsamy Al.. Controlling Green Sand Compactability. Modern Casting, 2002, 92(9): 37-39
24 Foundry M&T staff. Controlling Hot-Sand Improves Quality, Efficiency. Foundry Management & Technology, 2005, 133(1): 30-31
25 Piontkowski G.R. Choosing a Mold Compaction Method for Green Sand. Modern Casting, 2000, 90(9): 34
26 Strobl S.M., Schuster F.W. Gauging Green Sand Flowability Helps Predict Mold Quality. Modern Casting, 1997, 87(2): 48
27 刘业金, 等. 配砂系统的微机控制和 SSKJ 型砂水分自动控制仪的应用. 铸造, 1996(11): 27-30
28 陈绍华, 等. SSKJ 型砂水分自动控制仪. 热加工工艺, 1996(6):49-51
29 Baker Stephen G. Building the foundation for green sand. Modern Casting, 2005, 95(8): 26-29
30 Heine R. W., Schumacher J. S, Green R. A. Sand/metal Ratio and Moisture Content for Cooling of Green Sand. Transactions of the AmericanFoundrymen's Society, 1977, 84(26-30): 281-286
31 Fiser Richard E. Improving Green Sand Performance through Cooling. Modern Casting, 1990, 80(6): 65-66
32 张立光. 型砂性能在线检测与控制系统. 清华大学学报, 2000, 40(4): 1-3
33 张立光. 型砂性能智能化测控系统的研制. 铸造, 1999(4): 48-50
34 Lower G. W., Moore D. J., Pasternak L. Properties of Molding Sands, Effects of Sand Size Distribution and Mixing Energy Input. Transactions of the American Foundrymen's Society, 1983(91): 559-568
35 Sims B.J., Whitehead J.R. Cost-effective Control of Continuous Mixers. Foundry Trade Journal, 1988, 162(3377): 761-762
36 Wright George F. Precision Sand Castings an Involved Users Overview. Foundryman, 1995, 88(4): 118-121
37 Smith Robert J. Selecting a Muller for a Green Sand Operation. Modern Casting, 1990, 80(6): 66-68
38 Hohl Berthold. Practical Experience using the Evactherm? Vacuum Mixer, Proceedings of the 65th World Foundry Congress, Gyeongju, Korea, 2002: 803-812
39 Hohl Berthold. CAQ System Aids Sand Plant Performance. Foundry Manage- ment & Technology, 1994, 122(6): 34-38
40 胡彭生. 型砂. 上海科学技术出版社, 1980: 1, 16, 30
41 Tom Cobett. The ABCs of Green Sand. Foundry Management & Technology, 2002, 130(4): 24
42 Paul Carey. Sand/Binders/Sand Preparation/ & Coremaking. Foundry Manage- ment & Technology, 2002, 130(1): 39
43 Wang X.H., Fuh J.Y.H., Wong Y.S. Laser Sintering of Silica Sand-mechanism and Application to Sand Casting Mould. International Journal of Advanced Manufacturing Technology, 2003, 21(12): 1015-1020
44 王鸿禧. 膨润土. 地质出版社, 北京, 1980: 20-35
45 Stewart D.I. , Studds P.G., Cousens T.W. The Factors Controlling the Engineer- ing Properties of Bentonite-enhanced Sand. Applied Clay Science, 2003, 23(1-4): 97-110
46 Prayongphan Somchai, Ichikawa Yasuaki, Kawamura Katsuyuki, et al. Diffu- sion with Micro-sorption in Bentonite. Computational Mechanics, 2006, 37(4):369-380
47 Ito Hiroshi. Compaction Properties of Granular Bentonites. Applied Clay Science, 2006, 31(1): 47-55
48 G. Atun, A. Kilislioglu. Adsorption Behavior of Cesium on Montmorillonite- type Clay in the Presence of Potassium Ions. Journal of Radioanalytical and Nuclear Chemistry. 2003, 258(3): 605-611
49 H. Jobstmann, Balwant Singh. Cadmium Sorption by Hydroxy-Aluminium Interlayered Montmorillonite. Water, Air, & Soil Pollution, 2001, 31(4): 203-215
50 Heine R.W., Green R.A., Shih T.S. Understanding green strength and compacti- bility in green sand systems. Transactions of the American Foundrymen's Society, 1990: 245
51 Gonzáiez-Tovar Enrique, Jiménez-ángeles Felipe, Messina René, et al. A New Correlation Effect in the Helmholtz and Surface Potentials of the Electrical Double Layer. Journal of Chemical Physics, 2004, 120(20): 9782-9792
52 Palasantzas G., Backx G.M.E.A. Roughness Effects on the Double-layer Charge Capacitance: the Case of Helmholtz Layer Induced Roughness Attenuation. Surface Science, 2003, 540(2): 401
53 Giesbers Marcel, Kleijn J. Mieke, Cohen Stuart Martien A. The Electrical Double Layer on Gold Probed by Electrokinetic and Surface Force Measurements. Journal of Colloid and Interface Science, 2002, 248(1): 88-95
54 Caprio D., Borkowska Z., Stafiej J. Simple Extension of the Gouy-Chapman Theory Including Hard Sphere Effects. Journal of Electroanalytical Chemistry, 2003(540): 17
55 Quinn Anthony, Sedev Rossen, Ralston John. Influence of the Electrical Double Layer in Electrowetting. Journal of Physical Chemistry, 2003, 107(5): 1163-1169
56 Samaj L., Bajnok Z. Exactly Solvable Model of the Two-dimensional Electrical Double Layer. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 2005, 72(6): 1-14
57 Caprio D., Borkowska Z., Stafiej J. Specific Ionic Interactions within a Simple Extension of the Gouy-Chapman Theory Including Hard Sphere Effects. Journal of Electroanalytical Chemistry, 2004, 572(1): 51-59
58 Rytwo Giora. Applying a Gouy-Chapman-Stern Model for Adsorption of Organic Cations to Soils. Applied Clay Science, 2004, 24(3): 137
59 Kitamura Akira, Fujiwara Kenso, Yamamoto Tadashi. Analysis of Adsorption Behavior of Cations onto Quartz Surface by Electrical Double-layer Model. Journal of Nuclear Science and Technology, 1999, 36(12): 1167-1175
60 Persson C. Marcus, Jonsson A. Petra, Bergstr?m Magnus, et al. Testing the Gouy–Chapman Theory by Means of Surface Tension Measurements for SDS–NaCl–H2O Mixtures. Journal of Colloid & Interface Science, 2003, 267(1): 151
61 Gonzalez-Tovar E., Jimenez-Angeles F.; Messina R. A New Correlation Effect in the Helmholtz and Surface Potentials of the Electrical Double Layer. Journal of Chemical Physics, 2004, 120(20): 9782-9792
62 Semashko O.V., Brodskaya E.N., Us'Yarov O.G. Molecular Dynamics Simula- tion of the Electrical Double Layer of Spherical Macroion. Colloid Journal, 2005, 67(5): 625-630
63 Gillespie D., Valisko M., Boda D. Density Functional Theory of the Electrical Double Layer: the RFD functional. Journal of Physics: Condensed Matter, 2005, 17(42): 6609-6626
64 Lyklema Johannes, Duval Jér?me F.L. Hetero-interaction Between Gouy–Stern Double Layers: Charge and Potential Regulation. Advances in Colloid & Interface Science, 2005(114): 27-45
65 Williams G.D., Soper A.K., Skipper, N.T. High-resolution Structural Study of an Electrical Double Layer by Neutron Diffraction. Journal of Physical Chemistry, 1998, 102(45): 8945-8949
66 Tokudome K., Watanabe K., Ohtsuka, K. Evaluation of Electric Double Layer Capacitance of Fuel Cells by Electrical Equivalent Circuit. Memoirs of the Faculty of Engineering, 1998(27): 209-214
67 Briscoe W.H., Attard P. Counterion-only Electrical Double Layer: A Constrained Entropy Approach. Journal of Chemical Physics, 2002, 117(11-15): 5452-5464
68 Sekido Satoshi, Yoshino Youzo. Electrochemical Double Layer Capacitor. Electrochemistry, 2003, 71(7): 584-586
69 Lust E., Janes A.; Arulepp M. Influence of Solvent Nature on the Electro- chemical Parameters of Electrical Double Layer Capacitors. Journal of Electro-analytical Chemistry, 2004, 562(1): 33-42
70 Lust E., Janes A.; Arulepp M. Influence of Electrolyte Characteristics on the Electrochemical Parameters of Electrical Double Layer Capacitors. Journal of Solid State Electrochemistry, 2004, 8(7): 488-496
71 Allen J. Bard, Larry R. Faulkner. Electrochemical Methods Fundamentals and Applications (Second Edition). John Wiley and Sons, Inc., 2003: 9-11
72 Filz George M., Adams Tiffany, Davidson Richard R. Stability of Long Trenches in Sand Supported by Bentonite-water Slurry. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(9): 915-921
73 Moses Ray. ANN. Proceedings of the International Society for Optical Engineering, 1993(1721): 383-388
74 Moses Ray. ANN-architecture for Neural Nets. Third Workshop on Neural Networks: Academic/Industrial/NASA/ Defense, 1993: 383-388
75 Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design. PWS Publishing Company, 1996
76 Benjamin, Colin O., Chi Sheng-Chai, Gaber Tarek. Comparing BP and ART II Neural Network Classifiers for Facility Location. Computers & Industrial Engineering, 1995, 28(1): 43-50
77 Ilunga M., Stephenson D. Infilling Streamflow Data Using Feed-forward Back-propagation (BP) Artificial Neural Networks. Water SA, 2005, 31(2): 171-176
78 Liu, Z.L., Castagna J.P., Pan C.H. Impulse Response of BP Neural Networks and its Application to Seismic Wavelet Extraction. Proceedings of the International Joint Conference on Neural Networks, 1999(6): 3758-3761
79 Rabunal J.R., Dorado J.; Pazos A. A new Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP. Neural Computation, 2004, 16(7): 1483-523
80 Hinton G E. Learning Distributed Representations of Concepts. Proc Eighth Annual Conf Cognitive Science Socity, 1990
81 Lendaris George G., Rest Armin, Misley Thomas R. Improving ANN Genera- lization Using a Priori Knowledge to Pre-structure ANNs. Proceedings of IEEE International Conference on Neural Networks, 1997(1): 248-253
82 Scaletter R Z. An Emergence of Grandmother Memory in Feed ForwardNetworks: Learning with Noise and Forgetfulness in Connections Models and Their Implications. Congnitive Science, 1988
83 Von Leham A. Factors Influencing Learning by Back Propagation. IEEE Int Conf Neural Networks, Son Diegr, CAA, 1988
84 Decasper Dan S., Plattner Bernhard, Parulkar Guru M. Scalable Highperfor- mance Active Network Node. IEEE Network, 1999, 13(1): 8-19
85 Bartlett E.B. A Dynamic Node Architecture Scheme for Layered Neural Networks. Journal of Artificial Neural Networks, 1994, 1(2): 229-45
86 Choi J Y, Choi T H. Sensitivity Analysis of Multiplayer Percptron with Differentiable Activation Functions. IEEE Transon. Neur. Net. 1992(3): 101-107
87 Lendaris G.G. A Proposal for Indicating Quality of Generalization when Evaluating ANNs. Proceedings of International Joint Conference on Neural Networks, 1990(1): 709-713
88 Lou Y.F., Brunn P. An Offset Error Compensation Method for Improving ANN Accuracy when Used for Position Control of Precision Machinery. Neural Computing & Applications, 1998, 7(1): 90-95
89 Jabar W.A., El-Aawar H.B., Al-Mawsoof H.S. Op-amp Structures of Fixed Weight Applications of BP Neural Network. Proceedings of the International Conference TCSET (Modern Problems of Radio Engineering, Telecommunications and Computer Science) 2004: 70-75
90 Curran K., Li X., McCaughley N. Neural Network Face Detection. Imaging Science Journal, 2005, 53(2): 105-15
91 Utterson Eric. Linearizing PID Loop Control. ASHRAE Journal, 2003, 45(3): 40-45
92 Skoczowski S., Domek S., Pietrusewicz K. Model Following PID Control System. Kybernetes, 2003, 32(5): 818-28
93 Radke F., Isermann R. Parameter-adaptive PID-controller with Stepwise Parameter Optimization. Automatic, 1987, 23(4): 449-457
94 Yoshida T., Hashimoto S., Nakajima C. The MICREX-AX Advanced Informa- tion and Control System. Fuji Electric Review, 1997, 43(2): 36-41
95 Sasano K., Tanoshita M., Motosu H. Operator Station for the Integrated Control System MICREX-IX. Fuji Electric Review, 1995, 41(2): 41-45
96 Asami, K.. New YEWSERIES 80-SLCD/SLPC Adjuster. Instrumentation, 1986, 29(5): 53-60
97 何颖, 鹿蕾, 赵争鸣. PID 参数自整定方法概述. 现代电子技术, 2004(2): 20-23
98 Rice Wendell S. Take Control of PID Tuning. Plant Engineering (Barrington, Illinois), 2005, 59(9): 57-61
99 B. M. Mohan, Arpita Sinha. The Simplest Fuzzy PID Controllers: Mathematical Models and Stability Analysis. Soft Computing-A Fusion of Foundations, Methodologies and Applications. Springer-Verlag GmbH Online, 17 November 2005
100 G. M. Khoury, M.Saad, H.Y.Kanaan, et al. Fuzzy PID Control of a Five DOF Robot Arm. Journal of Intelligent and Robotic Systems. 2004, 40(3): 299-320
101 Behzad Moshiri, Farzan Rashidi. Self-tuning Based Fuzzy PID Controllers: Application to Control of Nonlinear HVAC Systems. Lecture Notes in Computer Science. Springer-Verlag GmbH Online, 2004(3177): 437
102 Mann G.K.I., Hu B.G., Gosine R.G. Fuzzy PID Controller Structures. Canadian Conference on Electrical and Computer Engineering, 1997(2): 788-791
103 Kazemian H.B. Self Organizing Fuzzy PID Controller. IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, 1998(1): 319-324
104 Sugisaka M., Mbaitiga Z. Design of PID Fuzzy Controller for Electric Vehicle Brake Control System Based on Parallel Structure of PI Fuzzy and PD Fuzzy. Transactions of the Institute of Electrical Engineers of Japan, 2005, 125(3): 245-252
105 Chatterjee A., Rakshit A., Ghoshal T.K. Fuzzy Dynamic Adaptation in PID Control. International Journal of Knowledge-Based Intelligent Engineering Systems, 2003, 7(4): 206-213
106 Galan P. Temperature Control: PID and Fuzzy Logic. Control Engineering Europe, 2004: 36-38
107 Gebov V.K. Main Principles at the Control of the Interuption for Processor with One Chip (mc68hc11). Proceedings of the 12th International Scientific and Applied Science Conference, 2003(2): 137-142
108 Raver N. FET Off-chip-drivers and Package Disturbs for Microprocessor.Proceedings of the 1984 Custom Integrated Circuits Conference, 1984: 574-579
109 Hirose Tetsuya, Yoshimura Ryuji, Ido Toru. Watch-dog Circuit for Quality Guarantee with Subthreshold MOSFET Current. IEICE Transactions on Electronics, 2004, E87-C(11): 1910-1914
110 Yasasri C. Watch Dog Timer for Programmable Logic Controllers. Journal of the Institution of Electronics & Telecommunication Engineers, 1990, 31(1): 21-24
2 Joyce Sara, Ziegler Mark, LaFay Vic. Optimizing New Sand and Core Sand Additions in Foundry Green Sand Systems. Foundry Management & Technology, 2005, 133(8): 46-59
3 Kvasha F.S. Influence of Sand Preparation System Factors on Consumption of Moulding Materials. Soviet Castings Technology (English Translation of Liteinoe Proizvodstvo), 1987(2): 60-64
4 H. G. Levelink. Steuerung des Sandqualittat in modernen Formanlagen. Giesserei, 1975, 62(5)
5 Lavington M. H. Sand Preparation-A Guide to Current Concepts in Mulling and Mixing. Foundry Trade Journal, 1983, 154(3260): 433-447
6 Foundry M&T staff. Key Fundamentals for Sand Preparation and Reclamation. Foundry Management & Technology, 1989, 117(4)
7 Rad Nader S., Tumay Mehmet T. Factors Affecting Sand Specimen Preparation by Raining. Geotechnical Testing Journal, 1987, 10(1): 31-37
8 Granlund M.J.. Understanding the Basics of Green Sand Testing. Modern Casting, 1999, 89(3): 38
9 袁永健. 混砂机配料称量及型砂水分在线自动检测装置. 中国铸造装备与技术, 2000 (5):48-50
10 T. Takemura. Theory to Estimate the Stress due to Moisture in Wood Using a Transmission-type Microwave Moisture Meter. Journal of Wood Science. 2003, 49(1): 11-17
11 王强, 杨占奎, 石晶玉等. 型砂水分红外光谱在线检测方法的探讨. 中国铸造装备与技术, 2003(6): 21-22
12 Pickrell K.C., Sergeant G.F., Wolfe M. Tips for Auditing Your Green Sand System. Modern Casting, 1999, 89(3): 30
13 Sindermann H. Green Sand Compaction by Impulse and Dynamic Molding. Transactions of the American Foundrymen's Society, 1995(102): 321
14 Bonacic-Mandinic Z., Budic I., Solenicki G. The Influence of Molding Sand on
the Compressive Stresses. Metalurgija, 2004, 43(1): 17-22
15 Kuerbis R., Vaid Y.P. Sand Sample Preparation - the Slurry Deposition Method. Soils and Foundations, 1988, 28(4): 107-118
16 Heine Hans J. Innovative Techniques Spur Green Molding Sand Preparation. Foundry Management & Technology, 1987, 115(8): 20-25
17 Li Dayong. Study on Testing Available Clay and Moisture Content of Green Sand Rapidly With New Method. 4th Asian Foundry Congress Proceedings (Australia), 1996,10
18 李大勇, 李庆春等. 基于双电源二次激励法的湿型砂组分快速预测用网络模型的研究. 电机与控制学报, 1999(9): 16-19
19 李剑中, 陆杰. 型砂紧实率快速测试方法的研究与实现. 制造业自动化, 2000, 22(5): 54-55
20 姚继成, 隋世城, 于海岩等. 型砂紧实率在线控制装置的研究及应用. 铸造, 2005, 54(9): 909-911
21 Schulze W.E. Green sand system controls. Modern Casting, 1990, 80(6): 68-69
22 T. Hanai, et al. Influence of Preparation Method of Test Specimen on Green Sand Measurements. Proceedings of the 65thWorld Foundry Congress, Gyeongju, Korea, 2002: 859-867
23 Alagarsamy Al.. Controlling Green Sand Compactability. Modern Casting, 2002, 92(9): 37-39
24 Foundry M&T staff. Controlling Hot-Sand Improves Quality, Efficiency. Foundry Management & Technology, 2005, 133(1): 30-31
25 Piontkowski G.R. Choosing a Mold Compaction Method for Green Sand. Modern Casting, 2000, 90(9): 34
26 Strobl S.M., Schuster F.W. Gauging Green Sand Flowability Helps Predict Mold Quality. Modern Casting, 1997, 87(2): 48
27 刘业金, 等. 配砂系统的微机控制和 SSKJ 型砂水分自动控制仪的应用. 铸造, 1996(11): 27-30
28 陈绍华, 等. SSKJ 型砂水分自动控制仪. 热加工工艺, 1996(6):49-51
29 Baker Stephen G. Building the foundation for green sand. Modern Casting, 2005, 95(8): 26-29
30 Heine R. W., Schumacher J. S, Green R. A. Sand/metal Ratio and Moisture Content for Cooling of Green Sand. Transactions of the AmericanFoundrymen's Society, 1977, 84(26-30): 281-286
31 Fiser Richard E. Improving Green Sand Performance through Cooling. Modern Casting, 1990, 80(6): 65-66
32 张立光. 型砂性能在线检测与控制系统. 清华大学学报, 2000, 40(4): 1-3
33 张立光. 型砂性能智能化测控系统的研制. 铸造, 1999(4): 48-50
34 Lower G. W., Moore D. J., Pasternak L. Properties of Molding Sands, Effects of Sand Size Distribution and Mixing Energy Input. Transactions of the American Foundrymen's Society, 1983(91): 559-568
35 Sims B.J., Whitehead J.R. Cost-effective Control of Continuous Mixers. Foundry Trade Journal, 1988, 162(3377): 761-762
36 Wright George F. Precision Sand Castings an Involved Users Overview. Foundryman, 1995, 88(4): 118-121
37 Smith Robert J. Selecting a Muller for a Green Sand Operation. Modern Casting, 1990, 80(6): 66-68
38 Hohl Berthold. Practical Experience using the Evactherm? Vacuum Mixer, Proceedings of the 65th World Foundry Congress, Gyeongju, Korea, 2002: 803-812
39 Hohl Berthold. CAQ System Aids Sand Plant Performance. Foundry Manage- ment & Technology, 1994, 122(6): 34-38
40 胡彭生. 型砂. 上海科学技术出版社, 1980: 1, 16, 30
41 Tom Cobett. The ABCs of Green Sand. Foundry Management & Technology, 2002, 130(4): 24
42 Paul Carey. Sand/Binders/Sand Preparation/ & Coremaking. Foundry Manage- ment & Technology, 2002, 130(1): 39
43 Wang X.H., Fuh J.Y.H., Wong Y.S. Laser Sintering of Silica Sand-mechanism and Application to Sand Casting Mould. International Journal of Advanced Manufacturing Technology, 2003, 21(12): 1015-1020
44 王鸿禧. 膨润土. 地质出版社, 北京, 1980: 20-35
45 Stewart D.I. , Studds P.G., Cousens T.W. The Factors Controlling the Engineer- ing Properties of Bentonite-enhanced Sand. Applied Clay Science, 2003, 23(1-4): 97-110
46 Prayongphan Somchai, Ichikawa Yasuaki, Kawamura Katsuyuki, et al. Diffu- sion with Micro-sorption in Bentonite. Computational Mechanics, 2006, 37(4):369-380
47 Ito Hiroshi. Compaction Properties of Granular Bentonites. Applied Clay Science, 2006, 31(1): 47-55
48 G. Atun, A. Kilislioglu. Adsorption Behavior of Cesium on Montmorillonite- type Clay in the Presence of Potassium Ions. Journal of Radioanalytical and Nuclear Chemistry. 2003, 258(3): 605-611
49 H. Jobstmann, Balwant Singh. Cadmium Sorption by Hydroxy-Aluminium Interlayered Montmorillonite. Water, Air, & Soil Pollution, 2001, 31(4): 203-215
50 Heine R.W., Green R.A., Shih T.S. Understanding green strength and compacti- bility in green sand systems. Transactions of the American Foundrymen's Society, 1990: 245
51 Gonzáiez-Tovar Enrique, Jiménez-ángeles Felipe, Messina René, et al. A New Correlation Effect in the Helmholtz and Surface Potentials of the Electrical Double Layer. Journal of Chemical Physics, 2004, 120(20): 9782-9792
52 Palasantzas G., Backx G.M.E.A. Roughness Effects on the Double-layer Charge Capacitance: the Case of Helmholtz Layer Induced Roughness Attenuation. Surface Science, 2003, 540(2): 401
53 Giesbers Marcel, Kleijn J. Mieke, Cohen Stuart Martien A. The Electrical Double Layer on Gold Probed by Electrokinetic and Surface Force Measurements. Journal of Colloid and Interface Science, 2002, 248(1): 88-95
54 Caprio D., Borkowska Z., Stafiej J. Simple Extension of the Gouy-Chapman Theory Including Hard Sphere Effects. Journal of Electroanalytical Chemistry, 2003(540): 17
55 Quinn Anthony, Sedev Rossen, Ralston John. Influence of the Electrical Double Layer in Electrowetting. Journal of Physical Chemistry, 2003, 107(5): 1163-1169
56 Samaj L., Bajnok Z. Exactly Solvable Model of the Two-dimensional Electrical Double Layer. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 2005, 72(6): 1-14
57 Caprio D., Borkowska Z., Stafiej J. Specific Ionic Interactions within a Simple Extension of the Gouy-Chapman Theory Including Hard Sphere Effects. Journal of Electroanalytical Chemistry, 2004, 572(1): 51-59
58 Rytwo Giora. Applying a Gouy-Chapman-Stern Model for Adsorption of Organic Cations to Soils. Applied Clay Science, 2004, 24(3): 137
59 Kitamura Akira, Fujiwara Kenso, Yamamoto Tadashi. Analysis of Adsorption Behavior of Cations onto Quartz Surface by Electrical Double-layer Model. Journal of Nuclear Science and Technology, 1999, 36(12): 1167-1175
60 Persson C. Marcus, Jonsson A. Petra, Bergstr?m Magnus, et al. Testing the Gouy–Chapman Theory by Means of Surface Tension Measurements for SDS–NaCl–H2O Mixtures. Journal of Colloid & Interface Science, 2003, 267(1): 151
61 Gonzalez-Tovar E., Jimenez-Angeles F.; Messina R. A New Correlation Effect in the Helmholtz and Surface Potentials of the Electrical Double Layer. Journal of Chemical Physics, 2004, 120(20): 9782-9792
62 Semashko O.V., Brodskaya E.N., Us'Yarov O.G. Molecular Dynamics Simula- tion of the Electrical Double Layer of Spherical Macroion. Colloid Journal, 2005, 67(5): 625-630
63 Gillespie D., Valisko M., Boda D. Density Functional Theory of the Electrical Double Layer: the RFD functional. Journal of Physics: Condensed Matter, 2005, 17(42): 6609-6626
64 Lyklema Johannes, Duval Jér?me F.L. Hetero-interaction Between Gouy–Stern Double Layers: Charge and Potential Regulation. Advances in Colloid & Interface Science, 2005(114): 27-45
65 Williams G.D., Soper A.K., Skipper, N.T. High-resolution Structural Study of an Electrical Double Layer by Neutron Diffraction. Journal of Physical Chemistry, 1998, 102(45): 8945-8949
66 Tokudome K., Watanabe K., Ohtsuka, K. Evaluation of Electric Double Layer Capacitance of Fuel Cells by Electrical Equivalent Circuit. Memoirs of the Faculty of Engineering, 1998(27): 209-214
67 Briscoe W.H., Attard P. Counterion-only Electrical Double Layer: A Constrained Entropy Approach. Journal of Chemical Physics, 2002, 117(11-15): 5452-5464
68 Sekido Satoshi, Yoshino Youzo. Electrochemical Double Layer Capacitor. Electrochemistry, 2003, 71(7): 584-586
69 Lust E., Janes A.; Arulepp M. Influence of Solvent Nature on the Electro- chemical Parameters of Electrical Double Layer Capacitors. Journal of Electro-analytical Chemistry, 2004, 562(1): 33-42
70 Lust E., Janes A.; Arulepp M. Influence of Electrolyte Characteristics on the Electrochemical Parameters of Electrical Double Layer Capacitors. Journal of Solid State Electrochemistry, 2004, 8(7): 488-496
71 Allen J. Bard, Larry R. Faulkner. Electrochemical Methods Fundamentals and Applications (Second Edition). John Wiley and Sons, Inc., 2003: 9-11
72 Filz George M., Adams Tiffany, Davidson Richard R. Stability of Long Trenches in Sand Supported by Bentonite-water Slurry. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(9): 915-921
73 Moses Ray. ANN. Proceedings of the International Society for Optical Engineering, 1993(1721): 383-388
74 Moses Ray. ANN-architecture for Neural Nets. Third Workshop on Neural Networks: Academic/Industrial/NASA/ Defense, 1993: 383-388
75 Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design. PWS Publishing Company, 1996
76 Benjamin, Colin O., Chi Sheng-Chai, Gaber Tarek. Comparing BP and ART II Neural Network Classifiers for Facility Location. Computers & Industrial Engineering, 1995, 28(1): 43-50
77 Ilunga M., Stephenson D. Infilling Streamflow Data Using Feed-forward Back-propagation (BP) Artificial Neural Networks. Water SA, 2005, 31(2): 171-176
78 Liu, Z.L., Castagna J.P., Pan C.H. Impulse Response of BP Neural Networks and its Application to Seismic Wavelet Extraction. Proceedings of the International Joint Conference on Neural Networks, 1999(6): 3758-3761
79 Rabunal J.R., Dorado J.; Pazos A. A new Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP. Neural Computation, 2004, 16(7): 1483-523
80 Hinton G E. Learning Distributed Representations of Concepts. Proc Eighth Annual Conf Cognitive Science Socity, 1990
81 Lendaris George G., Rest Armin, Misley Thomas R. Improving ANN Genera- lization Using a Priori Knowledge to Pre-structure ANNs. Proceedings of IEEE International Conference on Neural Networks, 1997(1): 248-253
82 Scaletter R Z. An Emergence of Grandmother Memory in Feed ForwardNetworks: Learning with Noise and Forgetfulness in Connections Models and Their Implications. Congnitive Science, 1988
83 Von Leham A. Factors Influencing Learning by Back Propagation. IEEE Int Conf Neural Networks, Son Diegr, CAA, 1988
84 Decasper Dan S., Plattner Bernhard, Parulkar Guru M. Scalable Highperfor- mance Active Network Node. IEEE Network, 1999, 13(1): 8-19
85 Bartlett E.B. A Dynamic Node Architecture Scheme for Layered Neural Networks. Journal of Artificial Neural Networks, 1994, 1(2): 229-45
86 Choi J Y, Choi T H. Sensitivity Analysis of Multiplayer Percptron with Differentiable Activation Functions. IEEE Transon. Neur. Net. 1992(3): 101-107
87 Lendaris G.G. A Proposal for Indicating Quality of Generalization when Evaluating ANNs. Proceedings of International Joint Conference on Neural Networks, 1990(1): 709-713
88 Lou Y.F., Brunn P. An Offset Error Compensation Method for Improving ANN Accuracy when Used for Position Control of Precision Machinery. Neural Computing & Applications, 1998, 7(1): 90-95
89 Jabar W.A., El-Aawar H.B., Al-Mawsoof H.S. Op-amp Structures of Fixed Weight Applications of BP Neural Network. Proceedings of the International Conference TCSET (Modern Problems of Radio Engineering, Telecommunications and Computer Science) 2004: 70-75
90 Curran K., Li X., McCaughley N. Neural Network Face Detection. Imaging Science Journal, 2005, 53(2): 105-15
91 Utterson Eric. Linearizing PID Loop Control. ASHRAE Journal, 2003, 45(3): 40-45
92 Skoczowski S., Domek S., Pietrusewicz K. Model Following PID Control System. Kybernetes, 2003, 32(5): 818-28
93 Radke F., Isermann R. Parameter-adaptive PID-controller with Stepwise Parameter Optimization. Automatic, 1987, 23(4): 449-457
94 Yoshida T., Hashimoto S., Nakajima C. The MICREX-AX Advanced Informa- tion and Control System. Fuji Electric Review, 1997, 43(2): 36-41
95 Sasano K., Tanoshita M., Motosu H. Operator Station for the Integrated Control System MICREX-IX. Fuji Electric Review, 1995, 41(2): 41-45
96 Asami, K.. New YEWSERIES 80-SLCD/SLPC Adjuster. Instrumentation, 1986, 29(5): 53-60
97 何颖, 鹿蕾, 赵争鸣. PID 参数自整定方法概述. 现代电子技术, 2004(2): 20-23
98 Rice Wendell S. Take Control of PID Tuning. Plant Engineering (Barrington, Illinois), 2005, 59(9): 57-61
99 B. M. Mohan, Arpita Sinha. The Simplest Fuzzy PID Controllers: Mathematical Models and Stability Analysis. Soft Computing-A Fusion of Foundations, Methodologies and Applications. Springer-Verlag GmbH Online, 17 November 2005
100 G. M. Khoury, M.Saad, H.Y.Kanaan, et al. Fuzzy PID Control of a Five DOF Robot Arm. Journal of Intelligent and Robotic Systems. 2004, 40(3): 299-320
101 Behzad Moshiri, Farzan Rashidi. Self-tuning Based Fuzzy PID Controllers: Application to Control of Nonlinear HVAC Systems. Lecture Notes in Computer Science. Springer-Verlag GmbH Online, 2004(3177): 437
102 Mann G.K.I., Hu B.G., Gosine R.G. Fuzzy PID Controller Structures. Canadian Conference on Electrical and Computer Engineering, 1997(2): 788-791
103 Kazemian H.B. Self Organizing Fuzzy PID Controller. IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, 1998(1): 319-324
104 Sugisaka M., Mbaitiga Z. Design of PID Fuzzy Controller for Electric Vehicle Brake Control System Based on Parallel Structure of PI Fuzzy and PD Fuzzy. Transactions of the Institute of Electrical Engineers of Japan, 2005, 125(3): 245-252
105 Chatterjee A., Rakshit A., Ghoshal T.K. Fuzzy Dynamic Adaptation in PID Control. International Journal of Knowledge-Based Intelligent Engineering Systems, 2003, 7(4): 206-213
106 Galan P. Temperature Control: PID and Fuzzy Logic. Control Engineering Europe, 2004: 36-38
107 Gebov V.K. Main Principles at the Control of the Interuption for Processor with One Chip (mc68hc11). Proceedings of the 12th International Scientific and Applied Science Conference, 2003(2): 137-142
108 Raver N. FET Off-chip-drivers and Package Disturbs for Microprocessor.Proceedings of the 1984 Custom Integrated Circuits Conference, 1984: 574-579
109 Hirose Tetsuya, Yoshimura Ryuji, Ido Toru. Watch-dog Circuit for Quality Guarantee with Subthreshold MOSFET Current. IEICE Transactions on Electronics, 2004, E87-C(11): 1910-1914
110 Yasasri C. Watch Dog Timer for Programmable Logic Controllers. Journal of the Institution of Electronics & Telecommunication Engineers, 1990, 31(1): 21-24