汽油机爆震诊断与控制策略的研究
摘要
近年来,随着石油资源的日益紧缺以及由此引发的燃油价格大幅提高,越来越多的发动机制造商开始采用更高的压缩比来达到提高燃油经济性的目的;同时,排放法规严格要求使用无铅汽油。这都导致汽油机发生爆震的可能性增大。爆震是限制火花点火发动机功率提高和经济性改善的一个重要因素,汽油机绝对不允许在强烈爆震的情况下工作,但当轻微爆震时,燃烧接近定容燃烧,汽油机的动力性与经济性均有所提高。现代汽油机大多都有爆震控制装置,但控制的原理、方法和精度一直在不断改进之中。研究爆震信号的特征提取方法,强度判定和诊断方法,并通过现代电子技术,实现基于爆震信号的实时、精确的点火闭环控制,使汽油机能够始终在轻微爆震附近(爆震边缘)工作,对进一步提高汽油机的性能、节省能源、减少排放具有重要的意义。在对汽油机进行爆震检测的基础上,研究了短时傅立叶变换(STFT)、平滑伪Wigner-Ville分布(SPWD)和离散小波变换(DWT)用于汽油机爆震特征提取的可行性和效果。发现:STFT和SPWD两种方法只有在适当滤掉信号的低频成分的前提下,才能从汽油机气缸压力信号和缸盖振动信号中提取出爆震特征;并且STFT和SPWD方法计算量大,耗时长;利用DWT方法可以直接从气缸压力信号和缸盖振动信号中有效地提取出轻微爆震特征,与前两种方法相比,速度更快,具有明显的优越性;而且,在只安装一只加速度传感器的情况下,利用DWT方法能够提取出4个气缸的爆震边缘特征。
本文对20个时域参数进行了分析研究,找出了其中适合描述小波变换特征域信号爆震特征的指标。针对其它指标存在的随工况变化比较敏感的缺陷,提出了一种相对能量指标,该指标将小波子带信号和原始振动信号关联起来,对工况变化具有较好的适应性,适合用于爆震判定。提出了用多个特征值组合起来作为爆震判定指标判定爆震的方法,以弥补原来单个指标判定爆震方法存在的不足。
在爆震诊断方法的研究中,将模糊聚类分析、神经网络方法应用到爆震诊断中,提出小波结合模糊C-均值聚类的爆震诊断方法和小波结合模糊神经网络的爆震诊断方法,给出了两种方法的原理,并分别用具体算例进行了检验,发现这两种方法都能够完成对汽油机燃烧模式的区分,并能诊断出爆震边缘燃烧。这两种方法相比,小波结合模糊C-均值聚类的爆震诊断方法更适合于用硬件实现。
为改进现有汽油机爆震控制策略在实时性方面存在的不足,本文提出一种开环与闭环相结合,在一个工作循环内完成从信号检测到点火提前角调整的爆震控制策略。研究了用神经网络代替传统的点火MAP图来确定基本点火提前角的方法,建立了小波结合模糊C-均值聚类算法的爆震运算模型,通过仿真运算验证了模型的正确性。
研究了爆震实时控制的实现方法,开发了爆震实时控制的两个关键部分:爆震运算单元和数据通讯单元。在FPGA硬件上实现了小波结合模糊C-均值的爆震运算算法,并对应用CAN总线实现FPGA与ECU之间的数据通讯的技术进行了研究。最后通过硬件试验验证了爆震运算单元和数据通讯单元的有效性。
Recently,with the increasing scarcity of oil resources and substantial increase in fuel prices, more and more engine manufacturers began to use a higher compression ratio to achieve the purpose of improving fuel economy. At the same time, the strict emissions regulations require the use of lead-free gasoline. These would increase the possibility of the gasoline engine knock. Knock is an important factor which has a strong impact on the power and fuel enconomy of spar-ignition engine. The engine can never be allowed to work under conditions of a strong knock. However, when slight knock occurs, the engine is close to isometric burning, so the economy and power of the engine are enhanced. Many of the modern gasoline engines are equipted with knock control system, but the control principles, methods and accuracy have been constantly improving. To research the methods of knock signal feature extraction, intensity determination and diagnosis, and to achieve the real-time and accurate closed-loop ignition control based on the knock signal by modern electronic technology are of great significance to improving the gasoline engine performance, saving energy, and reducing emission of the engine.
The article separately studied the feasibility and effectiveness of gasoline engine knock feature extraction methods, such as the Short-time Fourier Transform(STFT), smoothed pseudo Wigner-Ville distribution(SPWD) and the discrete wavelet transform(DWT). The study found that: when adopting the STFT and SPWD method, only under the condition of filtering out the low-frequency components of the signal can we extract the knock feature from the engine cylinder pressure signal and the cylinder head vibration signal; STFT and SPWD method also need a large amount of calculation and time consuming; DWT method can extract the slight knock feature well from the cylinder pressure signal and the cylinder head vibration signal; And compared with the previous two methods, DWT method is faster and has obvious superiority; Moreover, by adopting DWT method the slight knock feature of four cylinders can be extracted out under the condition of installing only one acceleration senor on the first cylinder head.
Twenty time-domain parameters are analyzed and the indexes suited to describe knock features of wavelet transform domain signal are found out. A relative energy index is proposed to overcome the defect that other indexes are sensitive to the changes of working conditions. The relative energy index associated wavelet sub-band signal with the original vibration signals, has better adaptability to work condition changes. It is suitable for knock judgement. The method of using multiple eigenvalues as knock judgement indexes is proposed to compensate for deficiency of the single index in knock judgement.
In the study of knock diagnosis methods, fuzzy cluster analysis and neural method are applied to knock diagnosis. The knock diagnosis methods of wavelet with fuzzy C-means clustering and wavelet with fuzzy neural network are put forward. The principles of the two methods also be discussed. The two methods are verified effective in distinguishing engine combustion modes by some simulation tests, and slight knock combustion is diagnosed by the two methods. Comparing the two methods, wavelet with fuzzy C-means method is more suitable for implementing by hardware in knock diagnosis.
In order to conquer the shortcomings in real-time performance of the existing gasoline engine knock control strategy, a new knock control strategy combined the open-loop control with closed-loop control is put forward. Using this control strategy can complete the works including signal detection and ignition advance angle adjustment in one engine work cycle. The mehod of using neural network instead of traditional experimental MAP to determine the basic ignition advance Angle is studied. The simulation model of knock operation which using wavelet combined with fuzzy C-means clustering algorithm is established. The validity of the model has been proved by the simulation computation.
Finally, the implement method of knock control is studied. Knock computing unit and data communication unit, which are two key parts of the knock control are developed. The wavelet combined with fuzzy C-means algorithm is realized in the FPGA hardware. Controller area network bus technology is applied to the data communicaiton betweeen FPGA and ECU. The effectiveness of the two units are verified by the hardware test.
本文对20个时域参数进行了分析研究,找出了其中适合描述小波变换特征域信号爆震特征的指标。针对其它指标存在的随工况变化比较敏感的缺陷,提出了一种相对能量指标,该指标将小波子带信号和原始振动信号关联起来,对工况变化具有较好的适应性,适合用于爆震判定。提出了用多个特征值组合起来作为爆震判定指标判定爆震的方法,以弥补原来单个指标判定爆震方法存在的不足。
在爆震诊断方法的研究中,将模糊聚类分析、神经网络方法应用到爆震诊断中,提出小波结合模糊C-均值聚类的爆震诊断方法和小波结合模糊神经网络的爆震诊断方法,给出了两种方法的原理,并分别用具体算例进行了检验,发现这两种方法都能够完成对汽油机燃烧模式的区分,并能诊断出爆震边缘燃烧。这两种方法相比,小波结合模糊C-均值聚类的爆震诊断方法更适合于用硬件实现。
为改进现有汽油机爆震控制策略在实时性方面存在的不足,本文提出一种开环与闭环相结合,在一个工作循环内完成从信号检测到点火提前角调整的爆震控制策略。研究了用神经网络代替传统的点火MAP图来确定基本点火提前角的方法,建立了小波结合模糊C-均值聚类算法的爆震运算模型,通过仿真运算验证了模型的正确性。
研究了爆震实时控制的实现方法,开发了爆震实时控制的两个关键部分:爆震运算单元和数据通讯单元。在FPGA硬件上实现了小波结合模糊C-均值的爆震运算算法,并对应用CAN总线实现FPGA与ECU之间的数据通讯的技术进行了研究。最后通过硬件试验验证了爆震运算单元和数据通讯单元的有效性。
Recently,with the increasing scarcity of oil resources and substantial increase in fuel prices, more and more engine manufacturers began to use a higher compression ratio to achieve the purpose of improving fuel economy. At the same time, the strict emissions regulations require the use of lead-free gasoline. These would increase the possibility of the gasoline engine knock. Knock is an important factor which has a strong impact on the power and fuel enconomy of spar-ignition engine. The engine can never be allowed to work under conditions of a strong knock. However, when slight knock occurs, the engine is close to isometric burning, so the economy and power of the engine are enhanced. Many of the modern gasoline engines are equipted with knock control system, but the control principles, methods and accuracy have been constantly improving. To research the methods of knock signal feature extraction, intensity determination and diagnosis, and to achieve the real-time and accurate closed-loop ignition control based on the knock signal by modern electronic technology are of great significance to improving the gasoline engine performance, saving energy, and reducing emission of the engine.
The article separately studied the feasibility and effectiveness of gasoline engine knock feature extraction methods, such as the Short-time Fourier Transform(STFT), smoothed pseudo Wigner-Ville distribution(SPWD) and the discrete wavelet transform(DWT). The study found that: when adopting the STFT and SPWD method, only under the condition of filtering out the low-frequency components of the signal can we extract the knock feature from the engine cylinder pressure signal and the cylinder head vibration signal; STFT and SPWD method also need a large amount of calculation and time consuming; DWT method can extract the slight knock feature well from the cylinder pressure signal and the cylinder head vibration signal; And compared with the previous two methods, DWT method is faster and has obvious superiority; Moreover, by adopting DWT method the slight knock feature of four cylinders can be extracted out under the condition of installing only one acceleration senor on the first cylinder head.
Twenty time-domain parameters are analyzed and the indexes suited to describe knock features of wavelet transform domain signal are found out. A relative energy index is proposed to overcome the defect that other indexes are sensitive to the changes of working conditions. The relative energy index associated wavelet sub-band signal with the original vibration signals, has better adaptability to work condition changes. It is suitable for knock judgement. The method of using multiple eigenvalues as knock judgement indexes is proposed to compensate for deficiency of the single index in knock judgement.
In the study of knock diagnosis methods, fuzzy cluster analysis and neural method are applied to knock diagnosis. The knock diagnosis methods of wavelet with fuzzy C-means clustering and wavelet with fuzzy neural network are put forward. The principles of the two methods also be discussed. The two methods are verified effective in distinguishing engine combustion modes by some simulation tests, and slight knock combustion is diagnosed by the two methods. Comparing the two methods, wavelet with fuzzy C-means method is more suitable for implementing by hardware in knock diagnosis.
In order to conquer the shortcomings in real-time performance of the existing gasoline engine knock control strategy, a new knock control strategy combined the open-loop control with closed-loop control is put forward. Using this control strategy can complete the works including signal detection and ignition advance angle adjustment in one engine work cycle. The mehod of using neural network instead of traditional experimental MAP to determine the basic ignition advance Angle is studied. The simulation model of knock operation which using wavelet combined with fuzzy C-means clustering algorithm is established. The validity of the model has been proved by the simulation computation.
Finally, the implement method of knock control is studied. Knock computing unit and data communication unit, which are two key parts of the knock control are developed. The wavelet combined with fuzzy C-means algorithm is realized in the FPGA hardware. Controller area network bus technology is applied to the data communicaiton betweeen FPGA and ECU. The effectiveness of the two units are verified by the hardware test.
引文
1李建权.火花点火发动机爆震燃烧的研究.天津大学博士学位论文. 1998: 58~70
2高磊. 468Q汽油机爆震检测和控制策略的研究.吉林大学硕士学位论文. 2007: 36~57
3 Heywood J B. Internal Combustion Engine Funda-mentals. Mc Gram-Hill Book Company. 1988: 138~196
4于秀敏,林学东,钱耀义,吕从民.汽油机爆震信号测量与应用分析.吉林工业大学学报. 2000, 3(2): 31~35
5 Hudson C etal. Knock measurement for fuel evaluation in spark ignition engines. Fuel. 2001, (80): 395~407
6张振东,王伯年,王祝炜.汽车发动机爆震检测与量化方法.上海理工大学学报. 1998, 12(4): 273~279
7张建峰.汽油机爆震特征提取与诊断研究.哈尔滨工业大学硕士学位论文. 2003: 6~23
8吴平友,黄河,程庆.汽油发动机爆震分析与控制.传动技术. 2003(3): 36~38
9刘金武,龚金科,谭理刚,高为国,倪小丹.基于多维模型的电喷汽油机MAP图的数值生成.汽车工程. 2006, 28(8): 719~724
10王宇鹏,爆震反馈控制汽油机点火ECU技术研究,河北工业大学硕士学位论文,2007: 8~15
11 Giorgio Rizzoni, Xing Cindy Chen. Detection of Internal Combustion Engine Knock Using Time-Frequency Distributions. IEEE Transaction on Instrumentation an Measurements. 1993, 19(3): 360~363
12 A. M. Zoubir. A Comparative Study of Multiple Test Based Techniques for Optimal Sensor Location for Knock Detection. Mechanical Systems and Signal Processing. 2000, 14(3): 353~369
13 M. D. Boland, A. M. Zoubir. Identification of Time-Varying Non-Linear Systems with Application to Knock Detection in Combustion Engines. IE EE Transaction on Instrumentation and Measurements. 1997, 15(3): 799~802
14 Leonardo Mangeruca, Alberto Ferrari, Alberto Sangiovanni-Vincentelli,Andrea Pierantoni, Michele Pennese. System Level Design of Embedded Controllers: Knock Detection, a Case Study in the Automotive Domain. Proceedings of the Design, Automation and Test in Europe Conference and Exhibition, 2003: 1~6
15 Curry. Three Dimensional Study of Flame Propagation in a SparkIgnition Engine. SAE Trans. 1963, (71): 69~78
16 S.Carstens Behrens, M.Wagner, Johann F.Bohme. Improved Knock Detection by Time Variant Filtered Structure-Bone Sound. IEEE Transaction on Instrumentation and Measurements. 1999, 26(1): 2255~2258
17 Mark Urlaub, Johann F. B?hme. Reconstruction of Pressure Signals on Structure-borne Sound for Knock Investigation. 2004 SAE World Congress Detroit. 2004, (3): 8~11
18 Sonke Carstens-Behrens, Michael Wagnel, Johann I. Bohme. Improved Knock Detection by Time Variant Filtered Structure-borne Sound. IEEE. 1999: 2255~2258
19 Olivier Boubal. Knock Detection in automobile engines. IEEE. Instrumentation & Measurement Magazine. 2000: 25~28
20刘海荣,于墩录,陈小迅,王仲芝.火花点火发动机爆震测量方法的初步研究.汽车技术. 2000, (3): 11~13
21 F. Millo, C. V. Ferraro. Knock in S. I. Engines: A Comparion between Different Techniques for Detection and Control. SAE . 982477
22蔡昌贵,黄韶炯.基于点火控制算法的汽油机爆震控制.农机化研究. 2006, (8): 200~202
23彭生辉.内燃机爆震与爆震传感器的性能研究.合肥工业大学硕士学位论文. 2005: 39~58
24吴平友,汽车发动机电控点火系统的分析与研究.上海交通大学硕士学位论文. 2004: 76~89
25 Olivier Grondin, Jonathan Chauvin, Fabrice Guillemin. Emmanuel Nguyen and Gilles Corde Combustion Parameters Estimation and Control Using Vibration Signal: Application to the Diesel HCCI Engine. Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, 2008: 5621~5627
26 Mathieu H., Jonathan C., Nicolas P.. Controlling the start of combustionon an HCCI Diesel engine. 2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA. 2008: 2084~2091
27许沧粟,邢建国.基于火花塞离子电流信号的发动机爆震检测研究.内燃机工程. 2002, (23): 12~15
28汪映,周龙保,吴筱敏.离子电流法检测发动机失火和爆震的研究.西安交通大学学报. 2002, (9): 36~40
29吴筱敏,汪映,李福明.采用离子电流法与机体振动法检测爆震的比较.西安交通大学学报. 2001, (10): 1059~1066
30 Guoming Zhu, Ibrahim Haskara, Jim Winkelman. Stochastic Limit Control and Its Application to Knock Limit Control Using Ionization Feedback. 2005 SAE World Congress. Detroit, Michigan, 2005: 18~28
31 Guoming Zhu, Ibrahim Haskara, Jim Winkelman. Closed-Loop Ignition Timing Control for SI Engines Using Ionization Current Feedback. IEEE. Transactions on Control Systems Technology. 2007, (15): 416~427
32刘海荣,盛凯夫,薛栋等.用火花塞光纤传感器测量发动机的爆震.光电工程. 2000, (5): 28~36
33高青,金英爱,孙志军,孙济美,方瑛.点燃式发动机爆震测量及其强度分析.燃烧科学与技术. 2003, (8): 335~388
34 Iwashita Y, Saito A. Observation of Knock Using a High Speed Shutter TV Camera System. SAE paper 831696
35 Eric Ollivier, Mohand Tazerout. A Non Intrusive Method for Knock Detection Based on the Exhaust Gas Temperature. SAE World Congress, Detroit, Michigan. 2005: 57~65
36 Collings N, Dinsdale S. Knock Detection by Means of the Spark Plug. SAE paper 820073
37 Harigaya Y. Heat Transfer in S.I. Engine under Knock Conditions. Automobile Technology. 2004, 155~162
38张金玉,张优云,谢友柏.时频分析方法在冲击故障早期诊断中的应用研究.振动工程学报. 2000, 13(2): 222~228
39马辉,赵鑫,赵群超等.时频分析在旋转机械故障诊断中的应用. 2007, 26(3): 61~64
40杨建国,林波,周瑞.基于分形的汽油机爆震边缘检测.内燃机学报. 2006, 24(1): 78~82
41 D. Konig. Application of Time-Frequency Analysis for Optimum Non-Equidistants Sampling of Automotive Signals Captured at Knock. IEEE Transaction on Instrumentation and Measurements. 1996, 15(3): 2746~2749
42 S. A. Dalianis. Time-Frequency Spectra for Frequency-Modulated Processes. Mechanical System and Signal Processing. 1997, 11(4): 621~635
43 Dan Lazarescu, Vasile Lazaresc. Knock Detection based on Power Spectrum Analysis. IEEE. 2005: 701~704
44王吉军,张冰焰,朱泓,马孝江.时频分析方法在机器故障诊断中的应用.大连理工大学学报. 1996, 36(3): 300~306
45朱利民,钟秉林,贾民平.振动信号短时分析方法及在机械故障诊断中的应用.机械工程学报. 2000, 13(3): 400~405
46耿遵敏,郑效忠,种方. Wigner分布的改进及其在非平稳振动分析及诊断中的应用.信号处理. 1995, 11(2): 116~123
47贾继德.往复机械非平稳信号的特征提取及诊断研究.中国科学技术大学博士学位论文. 2004: 45~78
48宦飞.基于时频分析的脑电图特征信息提取技术的研究.西安交通大学博士学位论文. 1999: 10~68
49 A. Claasen. The Wigner Distribution: a Tool for Time-Frequency Signal Analysis. IEEE Transaction on Signal Processing. 1994, 14(9): 372~389
50 Joon-Hyun Lee, Han-Jun Kim. Development of Enhanced Wigner-Ville Distribution Function. Mechanical System and Signal Processing. 2001, 15(2): 367~398
51 R. G. Baraniuk. Wigner-Ville Spectrum Estimation Via Wavelet Soft-Threshholding. IEEE Transaction on Signal Processing. 1994, 14(9): 452~455
52 Y. K. Park, Y. H. Kim. A Method to Minimize the Cross-Talk of the Wigner-Ville Distribution. Mechanical System and Signal Processing. 1997, 11(4): 547~559
53陈洁,沈远彤.基于小波包变换的威格纳分布交叉项的抑制.湖北大学学. 2006, 28(1): 4~6
54 F. Khandan, A. Ayatollahi. Performance region of center affine Filter for liminating of interference terms of discrete Wigner distribution. Image and Signal Processing and Analysis, 2003, (2): 621~625
55 F. S. Lu, C. X. Yang, P. L. Lin. An improved Wigner distribution based algorithm for signal identification. Underwater Technology, 2004, (4): 39~45
56林京.基于连续小波变换的信号检测技术与故障诊断.机械工程学报. 2000, 36(12): 95~100
57金岩,郝志勇.利用振动信号的小波变换识别内燃机噪声源的研究.内燃机工程. 2006, 27(2): 61~63
58 Y. Wu, R. Du. Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes. Mechanical Systems and Signal Processing. 2006, 10(1): 29~53
59 Jonathan M Borg, Ka C Cheok, George Saikalis, etal. Wavelet-Based Knock Detection with Fuzzy Logic. CIMSA 2005-IEEE International Conference on Computa-tional Intelligence for Measurement Systems and Applica-tions, GiardiniNaxos, 2005: 20~22
60刘世元,杜润生,杨叔子.小波包改进算法及其在柴油机振动诊断中的应用.内燃机学报. 2000, 18(1): 11~16
61 Sung Tae Park, Jinguo Yang. Engine Knock Detection Based on Wavelet Transform. Science and Technology. The 8th Russian-Korean International Symposium. 2004(3): 80~83
62 Jonathan M. Borg, George Saikalis, Shigeru Oh, Hitachi America Ltd. Research & Development, Knock Signal Analysis Using the Discrete Wavelet Transform. Ka C. Cheok, Oakland University, Michigan Copyright,2006 SAE World Congress Detroit, Michigan April 3-6, 2006
63 H. A. Noubari, G. A. Dumon. Towards an Improved Knock Detection and Quantification using Wavelets and Entropy-based Noise Compensation. SAE 2005 Noise and Vibration Conference and Exhibition Traverse City, Michigan May 16-19, 2005
64 F. Liu, X. E. Ruan. Wavelet-based diffusion approaches for signal denoising. Signal Processing, 2007, (87): 1138~1146
65 A. Azzalini, M. Farge, K. Schneider. Nonlinear wavelet thresholding: A recursive method todetermine the optimal denoising threshold. Appl. Comput. Harmon. Anal, 2005, (18): 177~185
66杨建国,刘晓峰,林波.基于DWT的汽油机爆震特征提取及爆震强度判定.内燃机学报. 2003, 21(3): 233~238
67杨建国,王彦岩,张继春,汽油机爆震边缘检测的研究.内燃机工程. 2007, 28(4): 73~75
68杨冬冬,尧命发,张波,张全长,黄锦成.基于离散小波变换的HCCI爆震特性参数研究.内燃机学报. 2007, 25(5): 402~408
69 Stephan Ker, FrM1dric Bonnardot, Laurent Duval. Algorithm Comparison For Real Time Time Knock Detection. ICASSP 2007: 397~400
70 Dan Lazarescu, Student Meniber IEEE, Vasile Lazarescu, Member IEEE, Mihaela Unbwreanu, Member lEEE. Knock Detection Based on SOM. 2004: 117~120
71 M. M. Ettefagh, M. H. Sadeghi, V. Pirouzpanah, H. Arjmandi Tash. Knock detection in spark ignition engines by vibration analysis of cylinder block: A parametric modeling approach. Mechanical Systems and Signal Processing 2008: 1495~1514
72武得钰,傅茂林,李健全等.火花点火发动机爆震强度评价指标的研究.内燃机学报, 1997, 15(1): 62~70
73李继军.火花点火发动机挤气射流—紊流型燃烧室及基于缸内压力波动检测的爆震控制系统研究.西安交通大学博士学位论文. 1995: 36~58
74 Ming Chuan Hung, Don Lin Yang. An efficient Fuzzy C-Means clustering algorithm. IEEE int.conf. 2001: 225 ~232
75 Hsiang-Chuan Liu, Jeng-Ming Yih, Der-Bang Wu, Shin-W.U Liu. Fuzzy C-mean algorithm based on "complete" mahalanobis distances. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC. 2008: 3569~3574
76 Jian Yu, Miin-Shen Yang. Optimality Test for generalized FCM and its application to parameter selection. IEEE Transactions on Fuzzy Systems. 2005: 164 ~176
77 Sui Wentao, Lu Changhou, Zhang Dan. Bearing Fault Diagnosis Based on Feature Weighted FCM Cluster Analysis. IEEE International Conference on Computer Science and Software Engineering. 2008: 518~521
78 Cai Lin, Huang Yuancan, Chen Jiabin. A Genetic-Based Fuzzy Clustering Algorithm for Fault Diagnosis in Satellite Attitude Determination System. Proceedings of the Sixth International Conference on Intelligent SystemsDesign and Applications. 2006: 834~837
79 Daniel F. Leite, Michel B. Hell, Patricia H. Diez, Bemardo S. L. Gariglio, Lucas O. Nascimento, Pyramo Costa Jr.. Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors. IEEE International Conference on Electric Machines & Drives. 2007: 202~207
80王洪海,袁维宝等.柴油机故障诊断中的模糊多元判别法.华北水利水电学院学报. 1997, 4(18): 43~46
81张雨.关联维分形与模糊聚类在柴油机工作状态监测中的应用.内燃机学报, 1998, 16 (2 ): 208~213
82朱建新,陈婕等.基于模糊聚类分析识别内燃机缸内燃烧异常的故障.浙江大学学报(自然科学版). 1999, 33(4): 422~426
83 A. de Lucas, A. Duran, M. Carmona, M. Lapuerta. Modeling diesel particulate emission with neural networks. Fuel. 2003, (80): 539~548
84 Vemuri A. T., Polycarpou M. M.. Neural-network-based robust fault diagnosis in robotic systems. IEEE Transactions on Neural Network, 1997, 8(6): 14~17
85 S. A. Grossberg. Nonlinear Neural Networks. Principles and Architectures. Neural Network, 2005, (1): 47~61
86 Chris M. A. Virtual Sensing: A Neural Network-based Intelligent Performance and Emission Prediction System for On-Board Diagnostics and Engine Control. SAE. 2005: 98~100
87袁海英,陈光,谢永乐.故障诊断中基于神经网络的特征提取方法研究.仪器仪表学报, 2007, 28(1): 90~94
88唐贵基,范德功,胡爱军等.基于小波包能量特征向量神经网络的旋转机械故障诊断.汽轮机技术, 2006, 48(3): 215~217
89曹龙汉,曹长修.基于粗糙集理论的柴油机神经网络故障诊断研究.内燃机学报. 2002, 20(4): 357~361
90冯志鹏,宋希庚.基于模糊C-均值-粗糙集-自适应模糊神经网络推理系统集成的故障诊断.内燃机学报. 2003, 21(4): 281~287
91韩宇石.电控汽油机爆震信号分析与检测.北京工业大学硕士学位论文. 2005: 67~79
92 Kenjiro Nakama. Study of Knock Control in Small GasolineEngines by Multi-Dimensional Simulation. SAE 2006: 2~34
93陈晓兵,基于嵌入式实时系统的爆震控制模块的研究.吉林大学硕士学位论文. 2006: 45~67
94麻友良.德国博世公司ME7.4电喷系统.汽车电器, 2005, (11): 23~25
95张翔,杨龙,孙明.汽车发动机管理系统制造商及其产品.汽车电器. 2006(8): 1~4
96李继军,陈宝书,武得钰,顾笑映,周龙保.汽油机爆震自动控制系统研究.系统工程理论与实践. 2000, (6): 115~117
97高青,金英爱,玄哲浩,李明,方瑛.发动机临界爆震控制特性研究.汽车工程. 2003, 25(6): 547~549
98高青,金英爱,李明,方瑛.点燃式发动机临界爆震控制及其特性吉林大学学报(工学版). 2004, 33(4): 7~11
99肖兵,李鹏,邱磊.发动机爆震检测技术研究.计算机测量与控制. 2005, 13(9): 910~913
100姜卓,卓斌.现代汽油机电控系统的爆震检测与控制.车用发动机. 2000, (127): 36~39
101段晖辉,姜卓,卓斌.汽油机集中式电子控制系统的研究.内燃机学报. 2001, (6): 63~69
102黄文虎,夏松波,刘瑞岩.设备故障诊断原理、技术及应用.科学出版社. 1996: 1~313
103 Joon-Hyun Lee, Han-Jun Kim. Development of Enhanced Wigner-Ville Distribution Function. Mechanical System and Signal Processing. 2001, 15(2): 367~398
104 Gregorio Andria, Mario Savino. Interpolated Smoothed Pseudo Wigner-Ville Distribution for Accurate Spectrum Analysis. IEEE Transactions on Instrumentation. 1996, 45(4): 818~823
105 K. M. Prabhu, R. Shanmuga. Fast Algorithm for Psendodicrete Wigner-Ville Distribution Using Moving Discrete Hartley Transform. IEEE Transaction on Signal Processing. 1996, 143(6): 383~386
106 J. Wahl, J. S. Bolton. The Application of the Wigner Distribution to the Identification of Structure-borne Noise Components. Journal of Sound and Vibration. 1993, 14(9): 101~122
107 S. G. Mallat. A Theory of Multi-resolution Signal Decomposition: The Wavelet Representation. IEEE Transaction on Pattern Analysis and MachineIntelligence. 1989, 11(7): 674~693
108杨建国.小波分析及其工程应用.机械工业出版社. 2005: 27~37
109 J. C. Bezdek. A Convergence Theorem for the Fuzzy Isodata Clustering Algorithm. IEEE Transactions PAMI. 1980, 2(1): 1~8
110李士勇.工程模糊数学及应用.哈尔滨工业大学出版社, 2004: 74~75
111诸克军,苏顺华,黎金玲.模糊C-均值中的最优聚类与最佳聚类数.系统工程理论与实践. 2005, 3(3): 54~56
112 L. O. Hall, B. Ozyurt, J. C. Bezdek. Clustering with a Genetically Optimized Approach. IEEE Tranactions on Evolutionary Computation. 1999, 3(2):103~112
113 A. O. Boudraa. Dynamic Estimation of Number of Clusters in Data Sets. Electronics Letters. 1999, 35 (19): 1606~1610
114 N. R. Pal, J. C. Bezdk. On Cluster Validity for the Fuzzy C-mean Model. IEEE Tranactions on Fuzzy Systems. 1995, 3(3): 370~379
115高新波.模糊聚类分析及其应用.西安电子科技大学出版社, 2004: 10~54
116田宝国,何友.人工神经网络在目标识别和分类中的应用.海军航空工程学院学报, 2005, 20(4): 401~404
117张翠平.电控汽油机燃油喷射及点火控制系统的设计与实验研究.太原理工大学博士学位论文. 2007: 34~45
118石宝玉.基于MFB的点火提前角神经网络控制.西华大学硕士学位论文. 2007: 23~41
119贺晓萍. FPG与单片机串行通信的实现.湖南理工学院学报. 2005: 18~25
120赵文兵,杨建宁. FIR滤波器的FPGA实现及其仿真研究.微计算机信息, 2005, 21(7):108~109
121李定川. FPGA技术在汽车电子中的应用.汽车电器. 2009, (8): 1~4
2高磊. 468Q汽油机爆震检测和控制策略的研究.吉林大学硕士学位论文. 2007: 36~57
3 Heywood J B. Internal Combustion Engine Funda-mentals. Mc Gram-Hill Book Company. 1988: 138~196
4于秀敏,林学东,钱耀义,吕从民.汽油机爆震信号测量与应用分析.吉林工业大学学报. 2000, 3(2): 31~35
5 Hudson C etal. Knock measurement for fuel evaluation in spark ignition engines. Fuel. 2001, (80): 395~407
6张振东,王伯年,王祝炜.汽车发动机爆震检测与量化方法.上海理工大学学报. 1998, 12(4): 273~279
7张建峰.汽油机爆震特征提取与诊断研究.哈尔滨工业大学硕士学位论文. 2003: 6~23
8吴平友,黄河,程庆.汽油发动机爆震分析与控制.传动技术. 2003(3): 36~38
9刘金武,龚金科,谭理刚,高为国,倪小丹.基于多维模型的电喷汽油机MAP图的数值生成.汽车工程. 2006, 28(8): 719~724
10王宇鹏,爆震反馈控制汽油机点火ECU技术研究,河北工业大学硕士学位论文,2007: 8~15
11 Giorgio Rizzoni, Xing Cindy Chen. Detection of Internal Combustion Engine Knock Using Time-Frequency Distributions. IEEE Transaction on Instrumentation an Measurements. 1993, 19(3): 360~363
12 A. M. Zoubir. A Comparative Study of Multiple Test Based Techniques for Optimal Sensor Location for Knock Detection. Mechanical Systems and Signal Processing. 2000, 14(3): 353~369
13 M. D. Boland, A. M. Zoubir. Identification of Time-Varying Non-Linear Systems with Application to Knock Detection in Combustion Engines. IE EE Transaction on Instrumentation and Measurements. 1997, 15(3): 799~802
14 Leonardo Mangeruca, Alberto Ferrari, Alberto Sangiovanni-Vincentelli,Andrea Pierantoni, Michele Pennese. System Level Design of Embedded Controllers: Knock Detection, a Case Study in the Automotive Domain. Proceedings of the Design, Automation and Test in Europe Conference and Exhibition, 2003: 1~6
15 Curry. Three Dimensional Study of Flame Propagation in a SparkIgnition Engine. SAE Trans. 1963, (71): 69~78
16 S.Carstens Behrens, M.Wagner, Johann F.Bohme. Improved Knock Detection by Time Variant Filtered Structure-Bone Sound. IEEE Transaction on Instrumentation and Measurements. 1999, 26(1): 2255~2258
17 Mark Urlaub, Johann F. B?hme. Reconstruction of Pressure Signals on Structure-borne Sound for Knock Investigation. 2004 SAE World Congress Detroit. 2004, (3): 8~11
18 Sonke Carstens-Behrens, Michael Wagnel, Johann I. Bohme. Improved Knock Detection by Time Variant Filtered Structure-borne Sound. IEEE. 1999: 2255~2258
19 Olivier Boubal. Knock Detection in automobile engines. IEEE. Instrumentation & Measurement Magazine. 2000: 25~28
20刘海荣,于墩录,陈小迅,王仲芝.火花点火发动机爆震测量方法的初步研究.汽车技术. 2000, (3): 11~13
21 F. Millo, C. V. Ferraro. Knock in S. I. Engines: A Comparion between Different Techniques for Detection and Control. SAE . 982477
22蔡昌贵,黄韶炯.基于点火控制算法的汽油机爆震控制.农机化研究. 2006, (8): 200~202
23彭生辉.内燃机爆震与爆震传感器的性能研究.合肥工业大学硕士学位论文. 2005: 39~58
24吴平友,汽车发动机电控点火系统的分析与研究.上海交通大学硕士学位论文. 2004: 76~89
25 Olivier Grondin, Jonathan Chauvin, Fabrice Guillemin. Emmanuel Nguyen and Gilles Corde Combustion Parameters Estimation and Control Using Vibration Signal: Application to the Diesel HCCI Engine. Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, 2008: 5621~5627
26 Mathieu H., Jonathan C., Nicolas P.. Controlling the start of combustionon an HCCI Diesel engine. 2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA. 2008: 2084~2091
27许沧粟,邢建国.基于火花塞离子电流信号的发动机爆震检测研究.内燃机工程. 2002, (23): 12~15
28汪映,周龙保,吴筱敏.离子电流法检测发动机失火和爆震的研究.西安交通大学学报. 2002, (9): 36~40
29吴筱敏,汪映,李福明.采用离子电流法与机体振动法检测爆震的比较.西安交通大学学报. 2001, (10): 1059~1066
30 Guoming Zhu, Ibrahim Haskara, Jim Winkelman. Stochastic Limit Control and Its Application to Knock Limit Control Using Ionization Feedback. 2005 SAE World Congress. Detroit, Michigan, 2005: 18~28
31 Guoming Zhu, Ibrahim Haskara, Jim Winkelman. Closed-Loop Ignition Timing Control for SI Engines Using Ionization Current Feedback. IEEE. Transactions on Control Systems Technology. 2007, (15): 416~427
32刘海荣,盛凯夫,薛栋等.用火花塞光纤传感器测量发动机的爆震.光电工程. 2000, (5): 28~36
33高青,金英爱,孙志军,孙济美,方瑛.点燃式发动机爆震测量及其强度分析.燃烧科学与技术. 2003, (8): 335~388
34 Iwashita Y, Saito A. Observation of Knock Using a High Speed Shutter TV Camera System. SAE paper 831696
35 Eric Ollivier, Mohand Tazerout. A Non Intrusive Method for Knock Detection Based on the Exhaust Gas Temperature. SAE World Congress, Detroit, Michigan. 2005: 57~65
36 Collings N, Dinsdale S. Knock Detection by Means of the Spark Plug. SAE paper 820073
37 Harigaya Y. Heat Transfer in S.I. Engine under Knock Conditions. Automobile Technology. 2004, 155~162
38张金玉,张优云,谢友柏.时频分析方法在冲击故障早期诊断中的应用研究.振动工程学报. 2000, 13(2): 222~228
39马辉,赵鑫,赵群超等.时频分析在旋转机械故障诊断中的应用. 2007, 26(3): 61~64
40杨建国,林波,周瑞.基于分形的汽油机爆震边缘检测.内燃机学报. 2006, 24(1): 78~82
41 D. Konig. Application of Time-Frequency Analysis for Optimum Non-Equidistants Sampling of Automotive Signals Captured at Knock. IEEE Transaction on Instrumentation and Measurements. 1996, 15(3): 2746~2749
42 S. A. Dalianis. Time-Frequency Spectra for Frequency-Modulated Processes. Mechanical System and Signal Processing. 1997, 11(4): 621~635
43 Dan Lazarescu, Vasile Lazaresc. Knock Detection based on Power Spectrum Analysis. IEEE. 2005: 701~704
44王吉军,张冰焰,朱泓,马孝江.时频分析方法在机器故障诊断中的应用.大连理工大学学报. 1996, 36(3): 300~306
45朱利民,钟秉林,贾民平.振动信号短时分析方法及在机械故障诊断中的应用.机械工程学报. 2000, 13(3): 400~405
46耿遵敏,郑效忠,种方. Wigner分布的改进及其在非平稳振动分析及诊断中的应用.信号处理. 1995, 11(2): 116~123
47贾继德.往复机械非平稳信号的特征提取及诊断研究.中国科学技术大学博士学位论文. 2004: 45~78
48宦飞.基于时频分析的脑电图特征信息提取技术的研究.西安交通大学博士学位论文. 1999: 10~68
49 A. Claasen. The Wigner Distribution: a Tool for Time-Frequency Signal Analysis. IEEE Transaction on Signal Processing. 1994, 14(9): 372~389
50 Joon-Hyun Lee, Han-Jun Kim. Development of Enhanced Wigner-Ville Distribution Function. Mechanical System and Signal Processing. 2001, 15(2): 367~398
51 R. G. Baraniuk. Wigner-Ville Spectrum Estimation Via Wavelet Soft-Threshholding. IEEE Transaction on Signal Processing. 1994, 14(9): 452~455
52 Y. K. Park, Y. H. Kim. A Method to Minimize the Cross-Talk of the Wigner-Ville Distribution. Mechanical System and Signal Processing. 1997, 11(4): 547~559
53陈洁,沈远彤.基于小波包变换的威格纳分布交叉项的抑制.湖北大学学. 2006, 28(1): 4~6
54 F. Khandan, A. Ayatollahi. Performance region of center affine Filter for liminating of interference terms of discrete Wigner distribution. Image and Signal Processing and Analysis, 2003, (2): 621~625
55 F. S. Lu, C. X. Yang, P. L. Lin. An improved Wigner distribution based algorithm for signal identification. Underwater Technology, 2004, (4): 39~45
56林京.基于连续小波变换的信号检测技术与故障诊断.机械工程学报. 2000, 36(12): 95~100
57金岩,郝志勇.利用振动信号的小波变换识别内燃机噪声源的研究.内燃机工程. 2006, 27(2): 61~63
58 Y. Wu, R. Du. Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes. Mechanical Systems and Signal Processing. 2006, 10(1): 29~53
59 Jonathan M Borg, Ka C Cheok, George Saikalis, etal. Wavelet-Based Knock Detection with Fuzzy Logic. CIMSA 2005-IEEE International Conference on Computa-tional Intelligence for Measurement Systems and Applica-tions, GiardiniNaxos, 2005: 20~22
60刘世元,杜润生,杨叔子.小波包改进算法及其在柴油机振动诊断中的应用.内燃机学报. 2000, 18(1): 11~16
61 Sung Tae Park, Jinguo Yang. Engine Knock Detection Based on Wavelet Transform. Science and Technology. The 8th Russian-Korean International Symposium. 2004(3): 80~83
62 Jonathan M. Borg, George Saikalis, Shigeru Oh, Hitachi America Ltd. Research & Development, Knock Signal Analysis Using the Discrete Wavelet Transform. Ka C. Cheok, Oakland University, Michigan Copyright,2006 SAE World Congress Detroit, Michigan April 3-6, 2006
63 H. A. Noubari, G. A. Dumon. Towards an Improved Knock Detection and Quantification using Wavelets and Entropy-based Noise Compensation. SAE 2005 Noise and Vibration Conference and Exhibition Traverse City, Michigan May 16-19, 2005
64 F. Liu, X. E. Ruan. Wavelet-based diffusion approaches for signal denoising. Signal Processing, 2007, (87): 1138~1146
65 A. Azzalini, M. Farge, K. Schneider. Nonlinear wavelet thresholding: A recursive method todetermine the optimal denoising threshold. Appl. Comput. Harmon. Anal, 2005, (18): 177~185
66杨建国,刘晓峰,林波.基于DWT的汽油机爆震特征提取及爆震强度判定.内燃机学报. 2003, 21(3): 233~238
67杨建国,王彦岩,张继春,汽油机爆震边缘检测的研究.内燃机工程. 2007, 28(4): 73~75
68杨冬冬,尧命发,张波,张全长,黄锦成.基于离散小波变换的HCCI爆震特性参数研究.内燃机学报. 2007, 25(5): 402~408
69 Stephan Ker, FrM1dric Bonnardot, Laurent Duval. Algorithm Comparison For Real Time Time Knock Detection. ICASSP 2007: 397~400
70 Dan Lazarescu, Student Meniber IEEE, Vasile Lazarescu, Member IEEE, Mihaela Unbwreanu, Member lEEE. Knock Detection Based on SOM. 2004: 117~120
71 M. M. Ettefagh, M. H. Sadeghi, V. Pirouzpanah, H. Arjmandi Tash. Knock detection in spark ignition engines by vibration analysis of cylinder block: A parametric modeling approach. Mechanical Systems and Signal Processing 2008: 1495~1514
72武得钰,傅茂林,李健全等.火花点火发动机爆震强度评价指标的研究.内燃机学报, 1997, 15(1): 62~70
73李继军.火花点火发动机挤气射流—紊流型燃烧室及基于缸内压力波动检测的爆震控制系统研究.西安交通大学博士学位论文. 1995: 36~58
74 Ming Chuan Hung, Don Lin Yang. An efficient Fuzzy C-Means clustering algorithm. IEEE int.conf. 2001: 225 ~232
75 Hsiang-Chuan Liu, Jeng-Ming Yih, Der-Bang Wu, Shin-W.U Liu. Fuzzy C-mean algorithm based on "complete" mahalanobis distances. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC. 2008: 3569~3574
76 Jian Yu, Miin-Shen Yang. Optimality Test for generalized FCM and its application to parameter selection. IEEE Transactions on Fuzzy Systems. 2005: 164 ~176
77 Sui Wentao, Lu Changhou, Zhang Dan. Bearing Fault Diagnosis Based on Feature Weighted FCM Cluster Analysis. IEEE International Conference on Computer Science and Software Engineering. 2008: 518~521
78 Cai Lin, Huang Yuancan, Chen Jiabin. A Genetic-Based Fuzzy Clustering Algorithm for Fault Diagnosis in Satellite Attitude Determination System. Proceedings of the Sixth International Conference on Intelligent SystemsDesign and Applications. 2006: 834~837
79 Daniel F. Leite, Michel B. Hell, Patricia H. Diez, Bemardo S. L. Gariglio, Lucas O. Nascimento, Pyramo Costa Jr.. Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors. IEEE International Conference on Electric Machines & Drives. 2007: 202~207
80王洪海,袁维宝等.柴油机故障诊断中的模糊多元判别法.华北水利水电学院学报. 1997, 4(18): 43~46
81张雨.关联维分形与模糊聚类在柴油机工作状态监测中的应用.内燃机学报, 1998, 16 (2 ): 208~213
82朱建新,陈婕等.基于模糊聚类分析识别内燃机缸内燃烧异常的故障.浙江大学学报(自然科学版). 1999, 33(4): 422~426
83 A. de Lucas, A. Duran, M. Carmona, M. Lapuerta. Modeling diesel particulate emission with neural networks. Fuel. 2003, (80): 539~548
84 Vemuri A. T., Polycarpou M. M.. Neural-network-based robust fault diagnosis in robotic systems. IEEE Transactions on Neural Network, 1997, 8(6): 14~17
85 S. A. Grossberg. Nonlinear Neural Networks. Principles and Architectures. Neural Network, 2005, (1): 47~61
86 Chris M. A. Virtual Sensing: A Neural Network-based Intelligent Performance and Emission Prediction System for On-Board Diagnostics and Engine Control. SAE. 2005: 98~100
87袁海英,陈光,谢永乐.故障诊断中基于神经网络的特征提取方法研究.仪器仪表学报, 2007, 28(1): 90~94
88唐贵基,范德功,胡爱军等.基于小波包能量特征向量神经网络的旋转机械故障诊断.汽轮机技术, 2006, 48(3): 215~217
89曹龙汉,曹长修.基于粗糙集理论的柴油机神经网络故障诊断研究.内燃机学报. 2002, 20(4): 357~361
90冯志鹏,宋希庚.基于模糊C-均值-粗糙集-自适应模糊神经网络推理系统集成的故障诊断.内燃机学报. 2003, 21(4): 281~287
91韩宇石.电控汽油机爆震信号分析与检测.北京工业大学硕士学位论文. 2005: 67~79
92 Kenjiro Nakama. Study of Knock Control in Small GasolineEngines by Multi-Dimensional Simulation. SAE 2006: 2~34
93陈晓兵,基于嵌入式实时系统的爆震控制模块的研究.吉林大学硕士学位论文. 2006: 45~67
94麻友良.德国博世公司ME7.4电喷系统.汽车电器, 2005, (11): 23~25
95张翔,杨龙,孙明.汽车发动机管理系统制造商及其产品.汽车电器. 2006(8): 1~4
96李继军,陈宝书,武得钰,顾笑映,周龙保.汽油机爆震自动控制系统研究.系统工程理论与实践. 2000, (6): 115~117
97高青,金英爱,玄哲浩,李明,方瑛.发动机临界爆震控制特性研究.汽车工程. 2003, 25(6): 547~549
98高青,金英爱,李明,方瑛.点燃式发动机临界爆震控制及其特性吉林大学学报(工学版). 2004, 33(4): 7~11
99肖兵,李鹏,邱磊.发动机爆震检测技术研究.计算机测量与控制. 2005, 13(9): 910~913
100姜卓,卓斌.现代汽油机电控系统的爆震检测与控制.车用发动机. 2000, (127): 36~39
101段晖辉,姜卓,卓斌.汽油机集中式电子控制系统的研究.内燃机学报. 2001, (6): 63~69
102黄文虎,夏松波,刘瑞岩.设备故障诊断原理、技术及应用.科学出版社. 1996: 1~313
103 Joon-Hyun Lee, Han-Jun Kim. Development of Enhanced Wigner-Ville Distribution Function. Mechanical System and Signal Processing. 2001, 15(2): 367~398
104 Gregorio Andria, Mario Savino. Interpolated Smoothed Pseudo Wigner-Ville Distribution for Accurate Spectrum Analysis. IEEE Transactions on Instrumentation. 1996, 45(4): 818~823
105 K. M. Prabhu, R. Shanmuga. Fast Algorithm for Psendodicrete Wigner-Ville Distribution Using Moving Discrete Hartley Transform. IEEE Transaction on Signal Processing. 1996, 143(6): 383~386
106 J. Wahl, J. S. Bolton. The Application of the Wigner Distribution to the Identification of Structure-borne Noise Components. Journal of Sound and Vibration. 1993, 14(9): 101~122
107 S. G. Mallat. A Theory of Multi-resolution Signal Decomposition: The Wavelet Representation. IEEE Transaction on Pattern Analysis and MachineIntelligence. 1989, 11(7): 674~693
108杨建国.小波分析及其工程应用.机械工业出版社. 2005: 27~37
109 J. C. Bezdek. A Convergence Theorem for the Fuzzy Isodata Clustering Algorithm. IEEE Transactions PAMI. 1980, 2(1): 1~8
110李士勇.工程模糊数学及应用.哈尔滨工业大学出版社, 2004: 74~75
111诸克军,苏顺华,黎金玲.模糊C-均值中的最优聚类与最佳聚类数.系统工程理论与实践. 2005, 3(3): 54~56
112 L. O. Hall, B. Ozyurt, J. C. Bezdek. Clustering with a Genetically Optimized Approach. IEEE Tranactions on Evolutionary Computation. 1999, 3(2):103~112
113 A. O. Boudraa. Dynamic Estimation of Number of Clusters in Data Sets. Electronics Letters. 1999, 35 (19): 1606~1610
114 N. R. Pal, J. C. Bezdk. On Cluster Validity for the Fuzzy C-mean Model. IEEE Tranactions on Fuzzy Systems. 1995, 3(3): 370~379
115高新波.模糊聚类分析及其应用.西安电子科技大学出版社, 2004: 10~54
116田宝国,何友.人工神经网络在目标识别和分类中的应用.海军航空工程学院学报, 2005, 20(4): 401~404
117张翠平.电控汽油机燃油喷射及点火控制系统的设计与实验研究.太原理工大学博士学位论文. 2007: 34~45
118石宝玉.基于MFB的点火提前角神经网络控制.西华大学硕士学位论文. 2007: 23~41
119贺晓萍. FPG与单片机串行通信的实现.湖南理工学院学报. 2005: 18~25
120赵文兵,杨建宁. FIR滤波器的FPGA实现及其仿真研究.微计算机信息, 2005, 21(7):108~109
121李定川. FPGA技术在汽车电子中的应用.汽车电器. 2009, (8): 1~4
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |