复杂工业系统基于小波网络与鲁棒估计建模方法研究
摘要
系统建模一向被视为控制与优化中必不可少的环节，实际工程中也一直奉
行“建模先行”的原则。随着科学技术的飞速发展及生产规模的不断扩大，
许多系统变得越来越复杂，尤其是世界范围内日益加剧的产业竞争以及人类
认识、改造和协调自然活动的深入，使系统建模遇到了前所未有的挑战，任
务艰巨，急待解决。
在这种情况下，单单靠传统控制理论的方法与技术远远不能满足当今复
杂工业过程提出的要求，必须从数学、计算机科学、信号处理技术等相关学
科汲取最新成果，开发出具有新构思的理论、技术和方法。
本文的工作旨在根据复杂工业过程的特点，基于小波网络和鲁棒估计理
论，并结合工程实际，针对复杂系统建模问题展开研究与讨论，具体内容包
括以下几个方面：
【1】系统地阐述了对复杂工业过程的认识，介绍了鲁棒估计理论的基本内
容，分析了小波尤其是小波网络在工业过程建模与控制中的应用情况，
指明了本文研究的内容和意义。
【2】分析了小波变换消除白噪声干扰的基本原理和方法，然后结合鲁棒统
计学的知识，提出一种可以抵抗服从污染分布干扰的小波变换，讨论
了小波系数的选取方法，分析了在线实时应用遇到的问题。这一研究
极有可能形成一种实用的信息净化技术。
【3】考虑到一般的滤波与辨识方法在非高斯假设下，不具有最优性这一情
况，研究了基于M估计的回归问题，然后在此基础上研究了鲁棒自适
应FIR滤波器和基于M估计的鲁棒卡尔曼滤波器，最后给出了一种用
于辨识非线性系统的结合小波的鲁棒自适应M估计方法，这种方法在
极大似然意义下是最优的。
【4】对各种小波网络进行了总结和概括，给出了一种通用的多尺度学习范
式；提出了一种结合遗传算法的小波网络分层结构设计方法，这种方
法先用修正的GS方法结合AIC构造小波网络，目的是获得经济的网络
u 浙江大学博士学位论文
——
结构和初始参数，再采用结合 GA的分层优化算法优化小波网络的两个
内部参数一平移和伸缩参数，目的是在不增加小波元的情况下获得更
高的精度。这一部分的内容对于各种小波网络的设计具有普遍的指导
意义。
【6】基于小波多分辨分析理论并结合面向控制的辨识思想，提出一种多率
采样系统分频段加权辨识方法。首先研究了基于小波的采样信息的。
致性重构问题，提出了基于小波网络的数据重构方法；然后给出一种
新颖的分频段加权辨识方法，实现对感兴趣频段的精确建模。这是一
种值得关注的系统建模方法，它为系统辨识和控制设计提供了一条新
的思路。
【0】基于小波网络和遗传算法，提出－种用于＊V系统建模的二次回归方
法。首先，利用小波网络构造参数子模型，并采用LS方法直接估计第
H代参数。然后利用遗传算法和AIC确定参数子模型的结构，以获得
最终用于表示uv系统的二次回归方程，实现时变系统的多尺度经济
建模。
【7】提出了两种新颖的小波网络，分别用于非线性静态和非线性动态系统
辨识。首先，分析了传统小波网络的不足，同时考虑到实际中，学习
样本可能被非高斯白噪声干扰的情况，提出用于辨识非线性静态系统
的鲁棒正交小波网络，并进行了辨识精度和收敛性分析；然后，提出
一种利于在线应用的可变正交小波网络，定义了结构可变策略和可变
操作，分析了误差的变化，给出了一种高效的参数调整策略。
【8】指出了工业软测量技术需要解决的关键问题，提出了多小波网络的概
念，给出了网络的结构和相应的构造方法；它弥补了多重神经网络的
不足：最后，提出了一种基于多小波网络的有约束的球磨机负荷软测
量方法。
ig】针对复杂工业过程中出现的混杂特性，提出了＊种结合小波网络的
Multi吧gent集成框架，设计了用于该框架的单个 Agent的基本模型，
分析了异构 Agent之间的关系，给出一种 Agent变迁策略，最后基于这
种集成技术，设计了钢球磨煤机智能控制系统。
最后，在总结全文的基础上，提出了研究过程中得出的若下思考，井对
未来的研究课题进行了展望。
System modeling is all long regarded as a critical step in automation and
optimization. odeling First?is also pursued all the times in industrial
applications. Along with technology development at very fast speed and
production scale-up, a lot of systems get more and more complicated. Especially
increasing hot industrial competition all over the world and people deeply
understanding, changing and harmonizing of nature, which make system modeling
encounter an unprecedented challenge, task is arduous and urgent to deal with.
Under such circumstance, requirements presented by complex industrial
process can be far from being satisfied merely with classical control theory, we
should draw up-to-date fruits from pertinent subjects such as math, computer
science, signal processing technology and so on to develop conceptive theory,
technology and method.
Combining with the characteristics of the complex industrial process, this
thesis aims at studying and discussing complex system modeling, based on
wavelet networks and robust estimation theory, and considering practical
engineering. The main contributions of the thesis are as follows:
[11 The understandings of complex systems are systematically set forth; the
basic contents of robust estimation theory are introduced; the applications
of wavelet analysis especially wavelet networks in the industrial process are
analyzed; the significance of the thesis抯 research is pointed out.
[21 The basic principle and method are analyzed of wavelet transformation to
eliminate white noise disturbance; then a kind of wavelet transform which
can resist the disturbance with contaminated distribution is presented, in
which the method to select wavelet coefficients is discussed and some
problem of online application are analyzed. This research is possible to lead
to a sort of practical information purified technology.
[31 Considering that the normal filtering and identification method is not
~1
Iv
optimal under non-gauss hypothesis, regress problem based on M
estimation is studied; then a robust adaptive FIR filter and robust Kalman
filter are studied. Finally a robust adaptive M estimation method combing
with wavelet for identifying nonlinear system is proposed, which is optimal
under maximum likelihood.
[41 Different wavelet networks are analyzed and generalized, and a common
multiscale learning paradigm is presented. Using the modified
Gram-Schmit algorithm combining with AIC to obtain a parsimonious
networks structure and initial parameters, a hierarchical structure design
method combing with GA for wavelet network is presented. Then two
interior parameters of wavelet networks, the dilation and translation
parameters will be optimized with GA combined hierarchical optimization
algorithm in order to get more accuracy approximation without adding any
wavelet. The results can provide a universal guidance for various wavelet
networks.
[51 Based on wavelet multiresolution analysis theory, a weighted band-wise
method for multirate sampled-data systems identification is proposed. First
the problem of multirate sampled-data reconstruction is studied and data
reconstruction method based on wavelet network is proposed. Then
行“建模先行”的原则。随着科学技术的飞速发展及生产规模的不断扩大，
许多系统变得越来越复杂，尤其是世界范围内日益加剧的产业竞争以及人类
认识、改造和协调自然活动的深入，使系统建模遇到了前所未有的挑战，任
务艰巨，急待解决。
在这种情况下，单单靠传统控制理论的方法与技术远远不能满足当今复
杂工业过程提出的要求，必须从数学、计算机科学、信号处理技术等相关学
科汲取最新成果，开发出具有新构思的理论、技术和方法。
本文的工作旨在根据复杂工业过程的特点，基于小波网络和鲁棒估计理
论，并结合工程实际，针对复杂系统建模问题展开研究与讨论，具体内容包
括以下几个方面：
【1】系统地阐述了对复杂工业过程的认识，介绍了鲁棒估计理论的基本内
容，分析了小波尤其是小波网络在工业过程建模与控制中的应用情况，
指明了本文研究的内容和意义。
【2】分析了小波变换消除白噪声干扰的基本原理和方法，然后结合鲁棒统
计学的知识，提出一种可以抵抗服从污染分布干扰的小波变换，讨论
了小波系数的选取方法，分析了在线实时应用遇到的问题。这一研究
极有可能形成一种实用的信息净化技术。
【3】考虑到一般的滤波与辨识方法在非高斯假设下，不具有最优性这一情
况，研究了基于M估计的回归问题，然后在此基础上研究了鲁棒自适
应FIR滤波器和基于M估计的鲁棒卡尔曼滤波器，最后给出了一种用
于辨识非线性系统的结合小波的鲁棒自适应M估计方法，这种方法在
极大似然意义下是最优的。
【4】对各种小波网络进行了总结和概括，给出了一种通用的多尺度学习范
式；提出了一种结合遗传算法的小波网络分层结构设计方法，这种方
法先用修正的GS方法结合AIC构造小波网络，目的是获得经济的网络
u 浙江大学博士学位论文
——
结构和初始参数，再采用结合 GA的分层优化算法优化小波网络的两个
内部参数一平移和伸缩参数，目的是在不增加小波元的情况下获得更
高的精度。这一部分的内容对于各种小波网络的设计具有普遍的指导
意义。
【6】基于小波多分辨分析理论并结合面向控制的辨识思想，提出一种多率
采样系统分频段加权辨识方法。首先研究了基于小波的采样信息的。
致性重构问题，提出了基于小波网络的数据重构方法；然后给出一种
新颖的分频段加权辨识方法，实现对感兴趣频段的精确建模。这是一
种值得关注的系统建模方法，它为系统辨识和控制设计提供了一条新
的思路。
【0】基于小波网络和遗传算法，提出－种用于＊V系统建模的二次回归方
法。首先，利用小波网络构造参数子模型，并采用LS方法直接估计第
H代参数。然后利用遗传算法和AIC确定参数子模型的结构，以获得
最终用于表示uv系统的二次回归方程，实现时变系统的多尺度经济
建模。
【7】提出了两种新颖的小波网络，分别用于非线性静态和非线性动态系统
辨识。首先，分析了传统小波网络的不足，同时考虑到实际中，学习
样本可能被非高斯白噪声干扰的情况，提出用于辨识非线性静态系统
的鲁棒正交小波网络，并进行了辨识精度和收敛性分析；然后，提出
一种利于在线应用的可变正交小波网络，定义了结构可变策略和可变
操作，分析了误差的变化，给出了一种高效的参数调整策略。
【8】指出了工业软测量技术需要解决的关键问题，提出了多小波网络的概
念，给出了网络的结构和相应的构造方法；它弥补了多重神经网络的
不足：最后，提出了一种基于多小波网络的有约束的球磨机负荷软测
量方法。
ig】针对复杂工业过程中出现的混杂特性，提出了＊种结合小波网络的
Multi吧gent集成框架，设计了用于该框架的单个 Agent的基本模型，
分析了异构 Agent之间的关系，给出一种 Agent变迁策略，最后基于这
种集成技术，设计了钢球磨煤机智能控制系统。
最后，在总结全文的基础上，提出了研究过程中得出的若下思考，井对
未来的研究课题进行了展望。
System modeling is all long regarded as a critical step in automation and
optimization. odeling First?is also pursued all the times in industrial
applications. Along with technology development at very fast speed and
production scale-up, a lot of systems get more and more complicated. Especially
increasing hot industrial competition all over the world and people deeply
understanding, changing and harmonizing of nature, which make system modeling
encounter an unprecedented challenge, task is arduous and urgent to deal with.
Under such circumstance, requirements presented by complex industrial
process can be far from being satisfied merely with classical control theory, we
should draw up-to-date fruits from pertinent subjects such as math, computer
science, signal processing technology and so on to develop conceptive theory,
technology and method.
Combining with the characteristics of the complex industrial process, this
thesis aims at studying and discussing complex system modeling, based on
wavelet networks and robust estimation theory, and considering practical
engineering. The main contributions of the thesis are as follows:
[11 The understandings of complex systems are systematically set forth; the
basic contents of robust estimation theory are introduced; the applications
of wavelet analysis especially wavelet networks in the industrial process are
analyzed; the significance of the thesis抯 research is pointed out.
[21 The basic principle and method are analyzed of wavelet transformation to
eliminate white noise disturbance; then a kind of wavelet transform which
can resist the disturbance with contaminated distribution is presented, in
which the method to select wavelet coefficients is discussed and some
problem of online application are analyzed. This research is possible to lead
to a sort of practical information purified technology.
[31 Considering that the normal filtering and identification method is not
~1
Iv
optimal under non-gauss hypothesis, regress problem based on M
estimation is studied; then a robust adaptive FIR filter and robust Kalman
filter are studied. Finally a robust adaptive M estimation method combing
with wavelet for identifying nonlinear system is proposed, which is optimal
under maximum likelihood.
[41 Different wavelet networks are analyzed and generalized, and a common
multiscale learning paradigm is presented. Using the modified
Gram-Schmit algorithm combining with AIC to obtain a parsimonious
networks structure and initial parameters, a hierarchical structure design
method combing with GA for wavelet network is presented. Then two
interior parameters of wavelet networks, the dilation and translation
parameters will be optimized with GA combined hierarchical optimization
algorithm in order to get more accuracy approximation without adding any
wavelet. The results can provide a universal guidance for various wavelet
networks.
[51 Based on wavelet multiresolution analysis theory, a weighted band-wise
method for multirate sampled-data systems identification is proposed. First
the problem of multirate sampled-data reconstruction is studied and data
reconstruction method based on wavelet network is proposed. Then
引文
Amir, B. Geva (1998) . ScaleNet-Multiscale Neural-Network architecture for time series predictive. IEEE Transaction on neural networks, 9(5) : 1471-1482
Astrom, K.J., and B. Wittenmark (1989) : Adaptive Control. Addison-WESLEY, Reading MA
Basseville, M.A. Benveniste and A.S. Willsky (1992) : Multiscale autoregressive processes. IEEE Trans. on Signal Processing, 40(8) : 1915-1954
Basseville, M.A. Benveniste, K.C.Chou, A.S. Golen, R.Nikoukhah and A.S. Willsky (1992) : Modeling and estimation of multiresolution stochastic processes. IEEE Trans. on Information Theory, 38(2) : 766-784
Baum, Haussler and Warmuth (1989) : What Size Net Gives Valid Generation? Neural Computation, vol 1: 151-160
Blumer, et al (1989) : Learnability and the Vapnik-Chervoneniks dimension. J. Assoc. Comput. Math, vol 36: 929-965
Branicky, M.S. (1998) : A unified Framework for hybrid control : model and optimal control theory . IEEE Trans. on AC, 43( 1) : 31-45
Bahavik, R. B. and G.Stephanopoulos (1993) : Wave-net: a mutiresolusion, hierarchical neural network with localized learning. AIChE Journal, 39(1) :57-81
Bakhtazad, A.A. Palazoglu, J.Romagoli (2000) : Process trend analysis using wavelet-based de-noising. Control Eng. Practice, vol 8: 657-663
Bakshi, B.R. (1999) : Multiscale analysis and modeling using wavelets. Journal of chemometrics, vol 13:415-431
Bakshi, B.R. (1998) : Multiscale PCA with application to multivariate statistacal process monitoring. AICHE J, vol 44: 1596-1610
Bakshi, B.R and George Stephanopoulos (1996) : Compression of chemical Process Data by Function Approximation and Feature Extraction. AICHE J, 42(2) : 477-492．
Benveniste, A. and K.J. Astrom (1993) : Facing the challenge of computer
science in the industrial application of control. Automatica, 29(5) : 1169-1175．
Bednar, J.B. and T.L.Watt (1984) : Alpha-Trimmed means and their Relationship to median filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-3(1) :145-153
Bor-Sen, C. and H. Wen-Sheng (1997) : Deconvolution filter design for fractal signal transaction systems: A multiscale kalman filter bank approach, IEEE Transaction on signal processing, 45(5) : 1359-1364
Bor-Sen Chen, and You-li chen (1995) : Multirate modeling of AR/MA stachastic signals and its application to the combined estimation-interpolation problem. IEEE Transction on signal processing, 43(10) : 2302-2312
Bruce.A, D.Donoho, H.Y. Gao, and R. Martin(1994) : Denoising and robust nonlinear wavelet analysis, in Wavelet Application, Pro. SPIE,2242: 325-336
Burrus,C.S., R.A. Gopinath, and H.Guo. (1998) : Introduction to wavelets and wavelet transforms. Prentice Hall,New Jersey Bryson, A. E. and Y. Ho (1975) : Applied Optimal Control. Washington, DC: Hemishere
Carrier, J. F. and G. Stephanopoulos (1998) : Wavelet-Based Modulation in Control-Relevant Process Identification. AIChE J, 44(2) : 341-360
Carmona, R.A. (1993) : Wavelet identification of transient in noise times series, Proceedings of SPIE, 2034: 392-400
Chris, C.H. and K.M. Bani (2000) : Bayesian wavelet networks for nonparametric regression. IEEE Transaction on Neural networks, 11(1) : 27-34
Cohen, A., I.Daubechies, and V.Pierre (1993) : Wavelets on the Interval and Fast Wavelet Transforms. Appl. Comput. Harmonic Anal, vol 1: 54-58
Cooper, D.J., L. Megan and R.F.Hinde (1992) : Disturbance pattern classification and neuro-adaptive control. IEEE control system, vol 4: 42-48
Chen, J. and D.Bruns (1995) : WaveARX neural network development for system identification using a systematic design synthesis. Ind. Eng.Chen.Res, 34(12) : 4420-4435
Chen, S., S. A. Billing, C. F. N. Cowan, and P. M. Grand (1990) : Non-linear systems identification using radial basis functions. Int. J. of Syst. Sci. 21,
2513-2539
Chen, S., S. A. Billing, C. F. N. Cowan, and P. M. Grand (1992) : Recursive hybrid algorithm for nonlinear system identification using radial basis function networks. Int. J. of control, 55(5) : 1051-1070
Chen, S., S. A. Billing (1992) : Neural networks for nonlinear dynamic system modeling and identification. Int. J. of control, 56(2) :319-346
Chou, K.C., A.S. Willsky and A. Benveniste (1994) : Multiscale recursive estimation, data fusion and regurilation. IEEE Trans, on AC, 38(3) : 464-478
Chou K.C., A.S. Willsky and R.Nikoukhan (1994) : Multiscale system, Kalman filter and Riccati equations. IEEE Trans. On Automatic Control, 38(3) : 479-492．
Chen, S. Y. Wu, and B. L. Luk (1999) : Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans. Neural Networks, 10(5) : 1239-1243
Doroslovachi, M. H.Fan and Y.Lei (1998) : Wavelet-based identification of line discrete time systems: robustness issue. Automatica, 34(12) :1637-1640
Daniel, W.C.H. and P.Zhang (2000) : A fuzzy-based wavelet network for dynamical system identification. Proceedings of the 3~(rd) Asian control conference July 4-7,Shanghai
Daiguji, M.O. Kudo and T.Wada (1997) : Application of wavelet analysis of fault detection in Oil refinery. Computers chem Engng, 21 ,S1117-S1122
Delyon B. A. Judishy and A.Benveniste (1995) : Accuracy analysis for wavelet approximation. IEEE Transaction on Neural Networks, 6(2) : 332-248
Daubechies, I.(1988) : Orthonarmal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics, 41(2) :909-996
Daubechies, I.(1990) : The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. On Information Theory, 36(5) : 961-1005．
Daubechies, I.(1996) : Where Do Wavelets Come From?-A personal point of view, Proceedings of the IEEE, 84(4) : 510-513
Daubechies, I. (1998) : Recent results in wavelet applications, SPIE 3391: 1-8
Donoho,D.L. (1995) : De-noising by soft-threholding. IEEE Transactions on Information Theory, 41(3) :613-627
Durrant-Whyte, H.F, B.Y.Rao, H.Hu (1990) : Toward a fully decentralized architecture for multisensor data fusion. In: Proc IEEE Int Conf Robotics
and Automation.
Donoho, D.L., I.M. Johnstone, G. Keyacharian, D. Picard (1993) : Density estimation by wavelet thresholding. Technical report, Stanford university, California
Dasaratha, V. S., C.S. Richard, B.B. Eric (1996) : Process modeling using stacked neural networks. AICHE Journal, 42(9) : 2529-2539
Eykhoff, P (1974) : System identification: parameter and state estimation. New York: Wiley
Erlich and Chazan et al (1997) : Lower bound on VC-dimension by local shattering. Neural Computation. Vol 9:771-776
Fei Mei,Man Zhihong,Xinghuo Yu. (1999) : Sliding mode control signal analysis. In: Han-Fu Chen, Dai-Zhan Cheng and Ji-Feng Zhang, eds 14~(th) world congress of IFAC, Beijing,Pergamon,vol.H,349-353
Freeman, T. G (1985) : Selecting the best linear transfer function model. Automatica, 21:361-370
Farkash, S. and S. Raz (1994) : Linear systems in Gabor time frequency space. IEEE Trans., on Automatic Control, 39(11) :1810-1826
Gopinath, R.A., and C.S. Bums (1995) : Theory of modalated filter banks and modulated wavelet tight frames. Appl. Comput.Harm, Ana., vol 2: 303-326
GUI Qing-ming, ZHANG Jian-jun (1998) : Robust biased estimation and its application in geodetic adjustments. Journal of Geodesy, vol 72: 430-435
HaiPeng Zhao and Joseph Bentsman (2000) : Wavelet-based identification of fast linear time-varying systems using function space methods. Proceedings of the American control conference Chicago, illinois: 939-943
His-Chin Hsin and Ching-Chung Li (1998) : Wavelet-based filtering in scale space for data fusion, SPIE 2569: 701-710
Hirchoren G.A. and C.E.D.Attellis, (1999) Estimation of fraction brownian motion with multiresolution Kalman filter banks. IEEE Transaction on Signal processing, 47(5) : 1431-1434
Huber, P. J ( 1981 ) : Robust Statistics Wiley New York.
Haweel, T.I and P.M. Clarkson (1992) : A class of order statistics LMS algorithms. IEEE Trans. Signal Procesing, 40(1) : 44-5 3
Hampel F.R. Ronchetti E.M. Rosseeuw P.J. and Stahel W.A (1986) : Robust statistics: the approach based on influence functions, New York :Wiley
Haiqing Wang, Zhihuan Song and Pingli (2000) ; An approach of reconstruction of high sample-rate data based on wavelet mutiresolution approximation. Information and Control, 29(4) : 315-319
Holden and Rayner (1995) : Generation and PAC learning: some new results for the class of generalized single-layer network. IEEE Trans. on neural networks, vol 6: 368-381
Jingchun Wang, Tihui Jin, Chongzhi Fang (1996) ; A data reconstruction method with wavelet transform, IFAC 13th Triennial world congress. San Francisco, USA, 343-348
Jeng Neng Hwang Shyh-Rong Lay, Martin Maechler, R.douglas Martin and James Schimert (1999) : Regression modeling in back-propagation and projection pursuit learning, IEEE Transaction on Neural networks 5(3) : 342-353
Kelly, S. E. (1996) : Gibbs phenomenon for wavelets. Appl. Comput. Harm. Anal, vol 3: 72-81
Krim, H. and C.Schick (1999) : Minimax description length for signal denoising and optimized representation. IEEE Transactions on Information Theory, 45(3) : 898-908
Kugarajah, T. and Q.Zhang (1995) ; Multiresolution Wavelet Frames. IEEE Trans, on Neural Networks, 6(6) : 1552-1557
Kovacevic, B.D., M. Durovic.S.T. Glavaski (1992) : On robust Kalman filtering. Int. J. Contr, vol 13: 547-562
Krishnakumar, K. (1989) : Micro-genetic algorithms for stationary and nonstationary function optimation, In Proc. SPIE on Intell. Cont. Adapt. Syst, 1196,289-296
Kulkarni, S.R and P. J. Ramadge (1996) : Multiple-model estimation with variable structure. IEEE Trans, on Automatic Control, 41(11) : 1594-1604
Kuokka, D.R(1995) : Communication infrastructure for concurrent engineering. Artificial Intelligent for Engineering Design, vol 9: 283-297
Lou Yiming, Song Zhihuan,Li Ping (1999) : Estimation and modeling of cross-directional basis weight on paper machines using multiresolution
analysis. In: Han-Fu Chen, Dai-Zhan Cheng and Ji-Feng Zhang, eds 14th world congress of IFAC, Beijing,Pergamon,vol.N,271-276
Lee, J.H, Z. Yu (1998) : Tuning of model predictive controllers for robust performance. Computers and Chenmical Engineering, 18(1) :15-37
Lee and Barlett (1995) : Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes. Neural computation, vol 7: 1040-1053
Lee, J.H, Chikkula (1995) : Improving computatinoal efficient of model predictive control algorithm using wavelet transformation. Int J control, 61(4) :859-883
Lang Hong, Guanrong Chen, and K.C. Charles (1998) : A filter bank based kalman filtering technique for wavelet estimation and decomposition of random signal. IEEE Transaction on circles and systems-II: Analog and digital signal processing, 45(2) : 237-241
Lang Hong (1994) : Multiresolutional Distribution Filtering. IEEE Transaction on Automatic control, 39(4) : 853-856
Lu,Y. et al (1992) : Reasoning about edges in scale space. IEEE Trans on PAMI, 14(4) :450-468
Lane, D.M, A. G. Mcfadzean (1994) : Distribute problem solving and real-time mechanisms in robot architectures. Engineering Application of Artificial Intelligent,7(2) : 105-117
Leonardo, M.R. (1999) : Unification of neural and wavelet networks and fuzzy system. IEEE Transaction on neural networks, 10(4) : 801-814
Mark Hansen and Bin Yu (2000) : Wavelet thresholding via MDL for natural Images. IEEE Tranctions on Information theory, 46(5) : 1778-1788
Martin Vetterli, and Cormac Herley (1992) : wavelet and filters banks: theory and design. IEEE transaction on signal processing, 40(9) : 2207-2228
Michael Nikolaou and Premkiran Vuthandam (1998) : FIR model identification: parsimony through kernel compression with wavelets. AICHE Journal, 44(1) : 141-150
Mohaned, N.N. and B.R. Bakshi (1999) : On-line multiscale filtering of random and gross errors without process models. AICHE Journal, 45 (5) : 1041-1058
Mallat, S and W.L. Hwang (1992) : Singularity detection and processing with
Wavelets. IEEE Transactions on Information Theory, 38(2) :617-643
Masreliez, C. J and R.D. Msrtin (1977) : Robust bayesian estimation for linear model and robustifying the Kalman filter. IEEE Trans on automatic control, vol22:361-371
Mallat,S.G (1989) : A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal. Machine Intell, PAMI-11,674-693
Mallat, S.G And W.L. Hwang (1991) : Singularity detection and processing with wavelets. IEEE Trans. on Inform. Theory, 38(2) : 617-636
Mallat, S.G and Z.F. Zhang (1993) : Matching pursuits with time-frequency dictionaries. IEEE Trans. on Signal Processing, vol 41: 3397-3415
Miao, X.A. (1992) : Normative-Descriptive approach to hierarchical team resource allocation. IEEE Trans. on Sys. Man And Cyb, 32(3) : 62-68
Macvoy, T.J. (1992) : Contemplative stance for chemical process. Automatica, 18(2) : 441-442
Narendra, K. S. and K. Parthasarathy (1990) : Identification and control of dynamical systems using neural networks. IEEE Trans. on Neural Networks, vol 1: 4-27
Narendra, K. S. and J. Balakrishnan (1995) : Adaptation and learning using multiple models, swithching and tuning. IEEE Contr Syst Mag, 15(3) :37-51
Narendra, K. S. and J. Balakrishnan (1995) : Adaptive control using multiple models. IEEE Trans on Automatic Control, 42(2) : 171-187
Neal, C.G., and L.W. Gary (1981) : A theoretical analysis of the properties of median filters. IEEE Transaction on Acoustics, speech, and signal processing, 29(6) : 1136-1140
Neumann, M., and R. Von Sachs (1995) : Wavelet thresholding: beyond the gaussian iid. situation, Lecture Notes in Statistics, Springer-Verlag, 103
Narendra, K.S. (1995) : Adaptation and learning using multiple models switching and tuning. IEEE Control Systems Magazine, 15(3) : 37-51
Pitas, I. and A,N. Venetsanopoulos (1991) : Adaptive filters based on order statistics. IEEE Trans. Signal Processing, 39,518-512
Paul, P. (1999) : Robust huber adaptive filter. IEEE Trans. on Signal Processing, 47(4) : 1129-1133
Partzen, E.(1962) : On estimation of a probability density function and mode. Ann. Math. Stalls, vol 33 : 1065-1076
Palavajjhala, S. et al. (1996) : Process identification using discrete wavelet transforms design of prefilters. AIChE Journal, 42(3) : 777-790
Rostein, H. and S. Raz (1995) : Gabor transform approximation of time-varying systems. Proceedings of the 1995 Americal Control Conference, pp:2424-2429
Rissanen, J. (1978) : Modeling By shortest Data Description. Automatica, vol 14: 465-471
Ronald, A.D, B.Jawerth, and J.L. Bradly (1992) : Image compression through wavelet transform coding. IEEE Transactions on Information Theory, 38(2) :719-746
Rosenblatt, M. (1956) : Remarks on some nonparametric estimates of a density function. Ann. Math. Statist, vol 27: 832-837
Safavi, A.A and J.A. Romagnoli (1997) : Comments on ' Nonlinear Black Box Modeling in system identification: A unified overview'. Automatica, 33(6) : 1197-1198
Sureshbabu, N. And J.A. Farrell (1999) : Wavelet-Based system identification for nonlinear control. IEEE Trans. on Automatic Control, 44(2) : 412-417
Safavi, A. A., J.Chen and J.A. Romagnoli (1997) : Wavelet based density estimation and application to process monitoring. AICHE J, 43(5) : 1227-1241
Safavi, A. A., D. Wang and J.A. Romagnoli (1999) : non-linear system identification using wavelet based on adaptive robust M-estimations, IFAC 14~(th) Trienninal world congress, Beijing.P.R. China, pp: 181-185
Schrama, R. J. P (1992) : Accurate identification for control: The necessity of an iterative schema. IEEE Trans. on Automatic Control, 37(7) : 991-994
SjOberg, J. et.al (1995) : Nonlinear black-box modeling in system identifications unified overview. Mtomatica, 31(12) : 1691-1724
Saridis, G.N (1987) : Knowledge, Implementation, Structure of intelligent control system, Pro of IEEE Int Symp on Intelligent Control
Thompson, M.L, M.A. Kramer (1994) : Modeling chemical process using prior
knowledge and neural networks. AICHE Journal, 40(8) : 1328-1340
Tetsuya Tabaru and Seiichi Shin (2000) : Improvement of wavelet based on dead time measurement method, Proceedings of the 3~(rd) Asian Control Conference, pp: 1929-1934
Tsatsanis, M. K, G.B. Giannakis (1993) : Time-Varying system identification and model validation using wavelets. IEEE Trans on Signal Processing, 41(12) : 3512-3523
Van den Hof, P. M. J. and R. J. P.Schrama (1995) : Identification and Control-Closed-loop Issues. Automatica, 31(12) : 1751-1770
Vapnik and Chervonenkis (1979) : On the uniform convergence of relative frequencies of events to their probabilities. Theory Pro. Appl, vol 16: 264-280
Valient, L.G (1984) : A Theory of the learnable. commun. ACM, vol 27: 1134-1143
Walter, G. G (1995) : A sampling theorem for wavelet subspace. IEEE Trans. on information theory, 38(2) :881-884
Witkin.A (1983) : Scale-space filtering, in proc.int.Joint conf.Artifical intell. pp.1019-1022
Wickerhauser, M. V. (1994) : Adapted wavelet analysis from theory to software. A.K. Peters
Wu X.C. and A. cinar (1996) : An adaptive robust M-estimation for nonparametric nonlinear system identification. J.Proc.contr, 6(4) : 233-239
Wolfgang, H., G. Kerkyacharian, D.Picard, and A. Tsybakow (1998) : Wavelets, approximation and statistical applications, Springer lecture note in statistics, Springer, New York
Wolpert, D. H. (1992) : Stacked Generalization. Neural Networks, 5(2) : 241-245
Wim Sweldens (1996) : Wavelets: what next? Proceedings of the IEEE, 84(4) : 680-685
Xu.Y, et al (1992) : Wavelet transform domain filters: a spatially selective noise filteration technique. IEEE Trans on IP, 13(6) :747-757
Yao Liang and W.Edward (1997) : Multiresolution learning paradigm and signal
prediction. IEEE Trans. Signal Processing, 45(11): 2858-2864
Yang, Z. J, and S. Sagata (1997): System impulse response identification using a multiresolution neural network. Automatica, 33(7): 1345-1350
Zhang, Q. and A. Benveniste (1992): Wavelet network. IEEE Trans. Neural Networks, 8(3): 889-898
Zhang, Q (1997): Using Wavelet Network in Nonparametric Estimation. IEEE Trans. Neural Networks, 8(2): 227-236
Zygmunt Hasiewicz (1999): Hammerstein system identification by the hear multiresolution approximation. Int. J Adapt. Control Process, vol 13, 691-717
Zhao, H.P. and B.Joseph (2000): wavelet-based identification of fast linear time-varying systems using function space methods, Proceedings of the American control conference chicago, illinois. June
Zhang, J., G.G. Walter and W.N. Lee (1995). Wavelet neural network for Function Learning. IEEE Trans. On Signal Processing, vol 43: 1485-1497
Zeljko, D.M., and B.D. Kovacevic (1999): Robust estimation with unknown noise statistics. IEEE Trans. on AC, 44(6): 1292-1296
宋执环(1997)：小波分析在工业过程辨识与控制中的应用研究．浙江大学博士论文
马伯文，曾洪(1990)：控制催化裂化装置粗汽油干点的数学模型．石油炼制，4：67—68．
王钦友，朱筠(1998)：基于Harr函数的分布参数系统辨识．信息与控制，27(5)：326—330．
李海青 黄志尧等(2000)：软测量技术原理及应用，北京：化学工业出版社
王行愚(1994)：系统辨识的若干新方向．控制理论与应用，11(1)：319—321
戴汝为(1991)：从定性到定量的综合集成技术，模式识别与人工智能，14：5-10
张贤达(1999)：现代信号处理，北京：清华大学出版社
杨福生(2000)：小波变换的工程分析与应用，北京：科学出版社
赵松年 熊小芸(1997)：子波变换与子波分析，北京：电子工业出版社
周江文(1989)：经典误差理论与抗差估计．测绘学报，18(2)：36-43
王海清(2000)：工业过程监测：基于小波和统计学的方法．浙江大学博士学位论文
高惠旋(1995)：统计计算，北京：北京大学出版社
张铃，张钹(1998)：前向神经网络设计问题的回顾与探索．计算机工程与科学，20(4)：1-10
方崇智(1988)：系统辨识，北京：清华大学出版社
王惠文(1999)：偏最小二乘回归方法及其应用，北京：国防工业出版社
吴耀军，陶宝祺，袁慎芳(1996)：样条小波网络．模式识别与人工智能，9(3)：228-233
吕立华(1999)：电站钢球磨煤机智能控制系统的研究．浙江大学工业控制技术研究所论文报告
仇林庆，吕立华等(2000)：钢球磨煤机出力测量．电站辅机，vol4，pp：34-36
吕立华，韩学军(1996)：FSSS锅炉炉膛安全保护系统的研制．中国仪器仪表学会学术年会论文集，pp：32-37
李祖枢，徐鸣，周其鉴(1990)：一种新型的仿人智能控制器．自动化学报，16(6)：503-509
范从振(1992)：锅炉原理，水利电力出版设，北京，1992
陈哲，冯天瑾(2000)：小波分析与神经网络结合的研究进展．电子科学学刊，22(3)：496-504
王群仙，李少远(2000)：小波分析及其在控制中的应用．控制与决策，15(4)：385-389
李化，岳刚(2000)：基于自适应小波网络的汽轮发电机组振动故障诊断方法的研究．电工技术学报．15(3)：57-60
王群仙，陈培强(1999)：基于小波网络动态补偿的广义预测控制器．自动化学报，25(5)：-701-704
李向武，韦岗(1998)：基于小波网络的动态系统辨识方法及应用．控制理论与应用，15(4)：494-500
钱峻，邵惠鹤(2000)：一种小波神经网络的在线建模和校正算法．模式识别与人工智能 13(1)：16-21
赵众，将慰孙(2000)：基于小波变换的工况区域识别．清华大学学报：自科版．40(1):91-94
叶昊，张永光(1998)：一种基于小波变换的导弹运输车辆故障诊断方法．自动化学报，24(3)：301-306．
李承暧，谭祖耀(1998)：新加坡国立大学的小波研究．世界科技研究与发展，20(1)：85-92
汤笑笑，李介谷(1998)：基于小波神经网络的系统辨识方法．信息与控制，27(4)：277-282
高协平，张钹(1998)：区间小波神经网络-理论与实现．软件学报，9(3)：217-221
刘山，吴铁军(1997)：一种基于小波逼近的稳定直接自适应控制算法．自动化学报．23(5)：636-640
徐长江，宋文忠(1997a)：基于小波变换估计传递函数．自动化学报，23(6)：835-838
徐长江，宋文忠(1997b)：基于小波变换的分频集员辨识．控制与决策，12(1)：48-52
李银国，张帮礼(1997)：小波神经网络及其结构设计方法．模式识别与人工智能．10(3)：197-205
王真理，李衍达(1995)：基于小波的信号部分重构与插值，清华大学学报：自科版，35(5)：67-72．
吴铁军(1994)：模型失配下故障诊断的小波分析．浙江大学学报，工业过程模型化及控制专辑，29：8-28．
钱峻，邵惠鹤(2000)：一种小波神经网络的在线建模和校正算法．模式识别与人工智能，13(1)：16-21
方浩，薛培鼎(2000)：系统辨识中小波基模型的优化．模式识别与人工智能，13(1)：60-63
袁廷奇(2000)：基于Haar小波变换的持续时间系统参数辨识．电气传动自动化，22(3)：22-23
张拥军 赵光宙(2000)：基于小波分解的时变系统辨识方法研究．浙江大学学报：工学版，034(005)：0541-0543
吕柏权，李天锋(1998)：一种基于小波网络的故障检测方法．控制理论与应用，15(5)：802-805
黄琳，秦化淑，郑应平，郑大钟(1993)：复杂控制系统理论：构想与前景，自动化学报，19(2)：129-136
钱学森(1991)：再谈开放的复杂巨系统．模式识别与人工智能，4(1)：1-4
冯纯伯(1994)：复杂工业生产过程的控制问题．控制理论与应用，11(1)：303-305
高为炳，霍伟(1994)：控制理论的发展与现状．控制理论与应用，11(1)：99—102
张钹 传统人工智能与控制技术的结合．控制理论与应用，11(1)：247—250
李人厚(1994)：复杂大系统的集成控制．控制理论与应用，11(1)：222—225
朱全民(1994)：非线性系统辨识．控制理论与应用，11(1)：661—651
李士勇(2000)：复杂系统、非线性科学与智能控制理论．计算机自动测量与控制，8(4)：1-3
徐道一(1999)：复杂性科学：21世纪的科学，科学时报，1999-10-23
Astrom, K.J., and B. Wittenmark (1989) : Adaptive Control. Addison-WESLEY, Reading MA
Basseville, M.A. Benveniste and A.S. Willsky (1992) : Multiscale autoregressive processes. IEEE Trans. on Signal Processing, 40(8) : 1915-1954
Basseville, M.A. Benveniste, K.C.Chou, A.S. Golen, R.Nikoukhah and A.S. Willsky (1992) : Modeling and estimation of multiresolution stochastic processes. IEEE Trans. on Information Theory, 38(2) : 766-784
Baum, Haussler and Warmuth (1989) : What Size Net Gives Valid Generation? Neural Computation, vol 1: 151-160
Blumer, et al (1989) : Learnability and the Vapnik-Chervoneniks dimension. J. Assoc. Comput. Math, vol 36: 929-965
Branicky, M.S. (1998) : A unified Framework for hybrid control : model and optimal control theory . IEEE Trans. on AC, 43( 1) : 31-45
Bahavik, R. B. and G.Stephanopoulos (1993) : Wave-net: a mutiresolusion, hierarchical neural network with localized learning. AIChE Journal, 39(1) :57-81
Bakhtazad, A.A. Palazoglu, J.Romagoli (2000) : Process trend analysis using wavelet-based de-noising. Control Eng. Practice, vol 8: 657-663
Bakshi, B.R. (1999) : Multiscale analysis and modeling using wavelets. Journal of chemometrics, vol 13:415-431
Bakshi, B.R. (1998) : Multiscale PCA with application to multivariate statistacal process monitoring. AICHE J, vol 44: 1596-1610
Bakshi, B.R and George Stephanopoulos (1996) : Compression of chemical Process Data by Function Approximation and Feature Extraction. AICHE J, 42(2) : 477-492．
Benveniste, A. and K.J. Astrom (1993) : Facing the challenge of computer
science in the industrial application of control. Automatica, 29(5) : 1169-1175．
Bednar, J.B. and T.L.Watt (1984) : Alpha-Trimmed means and their Relationship to median filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-3(1) :145-153
Bor-Sen, C. and H. Wen-Sheng (1997) : Deconvolution filter design for fractal signal transaction systems: A multiscale kalman filter bank approach, IEEE Transaction on signal processing, 45(5) : 1359-1364
Bor-Sen Chen, and You-li chen (1995) : Multirate modeling of AR/MA stachastic signals and its application to the combined estimation-interpolation problem. IEEE Transction on signal processing, 43(10) : 2302-2312
Bruce.A, D.Donoho, H.Y. Gao, and R. Martin(1994) : Denoising and robust nonlinear wavelet analysis, in Wavelet Application, Pro. SPIE,2242: 325-336
Burrus,C.S., R.A. Gopinath, and H.Guo. (1998) : Introduction to wavelets and wavelet transforms. Prentice Hall,New Jersey Bryson, A. E. and Y. Ho (1975) : Applied Optimal Control. Washington, DC: Hemishere
Carrier, J. F. and G. Stephanopoulos (1998) : Wavelet-Based Modulation in Control-Relevant Process Identification. AIChE J, 44(2) : 341-360
Carmona, R.A. (1993) : Wavelet identification of transient in noise times series, Proceedings of SPIE, 2034: 392-400
Chris, C.H. and K.M. Bani (2000) : Bayesian wavelet networks for nonparametric regression. IEEE Transaction on Neural networks, 11(1) : 27-34
Cohen, A., I.Daubechies, and V.Pierre (1993) : Wavelets on the Interval and Fast Wavelet Transforms. Appl. Comput. Harmonic Anal, vol 1: 54-58
Cooper, D.J., L. Megan and R.F.Hinde (1992) : Disturbance pattern classification and neuro-adaptive control. IEEE control system, vol 4: 42-48
Chen, J. and D.Bruns (1995) : WaveARX neural network development for system identification using a systematic design synthesis. Ind. Eng.Chen.Res, 34(12) : 4420-4435
Chen, S., S. A. Billing, C. F. N. Cowan, and P. M. Grand (1990) : Non-linear systems identification using radial basis functions. Int. J. of Syst. Sci. 21,
2513-2539
Chen, S., S. A. Billing, C. F. N. Cowan, and P. M. Grand (1992) : Recursive hybrid algorithm for nonlinear system identification using radial basis function networks. Int. J. of control, 55(5) : 1051-1070
Chen, S., S. A. Billing (1992) : Neural networks for nonlinear dynamic system modeling and identification. Int. J. of control, 56(2) :319-346
Chou, K.C., A.S. Willsky and A. Benveniste (1994) : Multiscale recursive estimation, data fusion and regurilation. IEEE Trans, on AC, 38(3) : 464-478
Chou K.C., A.S. Willsky and R.Nikoukhan (1994) : Multiscale system, Kalman filter and Riccati equations. IEEE Trans. On Automatic Control, 38(3) : 479-492．
Chen, S. Y. Wu, and B. L. Luk (1999) : Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans. Neural Networks, 10(5) : 1239-1243
Doroslovachi, M. H.Fan and Y.Lei (1998) : Wavelet-based identification of line discrete time systems: robustness issue. Automatica, 34(12) :1637-1640
Daniel, W.C.H. and P.Zhang (2000) : A fuzzy-based wavelet network for dynamical system identification. Proceedings of the 3~(rd) Asian control conference July 4-7,Shanghai
Daiguji, M.O. Kudo and T.Wada (1997) : Application of wavelet analysis of fault detection in Oil refinery. Computers chem Engng, 21 ,S1117-S1122
Delyon B. A. Judishy and A.Benveniste (1995) : Accuracy analysis for wavelet approximation. IEEE Transaction on Neural Networks, 6(2) : 332-248
Daubechies, I.(1988) : Orthonarmal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics, 41(2) :909-996
Daubechies, I.(1990) : The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. On Information Theory, 36(5) : 961-1005．
Daubechies, I.(1996) : Where Do Wavelets Come From?-A personal point of view, Proceedings of the IEEE, 84(4) : 510-513
Daubechies, I. (1998) : Recent results in wavelet applications, SPIE 3391: 1-8
Donoho,D.L. (1995) : De-noising by soft-threholding. IEEE Transactions on Information Theory, 41(3) :613-627
Durrant-Whyte, H.F, B.Y.Rao, H.Hu (1990) : Toward a fully decentralized architecture for multisensor data fusion. In: Proc IEEE Int Conf Robotics
and Automation.
Donoho, D.L., I.M. Johnstone, G. Keyacharian, D. Picard (1993) : Density estimation by wavelet thresholding. Technical report, Stanford university, California
Dasaratha, V. S., C.S. Richard, B.B. Eric (1996) : Process modeling using stacked neural networks. AICHE Journal, 42(9) : 2529-2539
Eykhoff, P (1974) : System identification: parameter and state estimation. New York: Wiley
Erlich and Chazan et al (1997) : Lower bound on VC-dimension by local shattering. Neural Computation. Vol 9:771-776
Fei Mei,Man Zhihong,Xinghuo Yu. (1999) : Sliding mode control signal analysis. In: Han-Fu Chen, Dai-Zhan Cheng and Ji-Feng Zhang, eds 14~(th) world congress of IFAC, Beijing,Pergamon,vol.H,349-353
Freeman, T. G (1985) : Selecting the best linear transfer function model. Automatica, 21:361-370
Farkash, S. and S. Raz (1994) : Linear systems in Gabor time frequency space. IEEE Trans., on Automatic Control, 39(11) :1810-1826
Gopinath, R.A., and C.S. Bums (1995) : Theory of modalated filter banks and modulated wavelet tight frames. Appl. Comput.Harm, Ana., vol 2: 303-326
GUI Qing-ming, ZHANG Jian-jun (1998) : Robust biased estimation and its application in geodetic adjustments. Journal of Geodesy, vol 72: 430-435
HaiPeng Zhao and Joseph Bentsman (2000) : Wavelet-based identification of fast linear time-varying systems using function space methods. Proceedings of the American control conference Chicago, illinois: 939-943
His-Chin Hsin and Ching-Chung Li (1998) : Wavelet-based filtering in scale space for data fusion, SPIE 2569: 701-710
Hirchoren G.A. and C.E.D.Attellis, (1999) Estimation of fraction brownian motion with multiresolution Kalman filter banks. IEEE Transaction on Signal processing, 47(5) : 1431-1434
Huber, P. J ( 1981 ) : Robust Statistics Wiley New York.
Haweel, T.I and P.M. Clarkson (1992) : A class of order statistics LMS algorithms. IEEE Trans. Signal Procesing, 40(1) : 44-5 3
Hampel F.R. Ronchetti E.M. Rosseeuw P.J. and Stahel W.A (1986) : Robust statistics: the approach based on influence functions, New York :Wiley
Haiqing Wang, Zhihuan Song and Pingli (2000) ; An approach of reconstruction of high sample-rate data based on wavelet mutiresolution approximation. Information and Control, 29(4) : 315-319
Holden and Rayner (1995) : Generation and PAC learning: some new results for the class of generalized single-layer network. IEEE Trans. on neural networks, vol 6: 368-381
Jingchun Wang, Tihui Jin, Chongzhi Fang (1996) ; A data reconstruction method with wavelet transform, IFAC 13th Triennial world congress. San Francisco, USA, 343-348
Jeng Neng Hwang Shyh-Rong Lay, Martin Maechler, R.douglas Martin and James Schimert (1999) : Regression modeling in back-propagation and projection pursuit learning, IEEE Transaction on Neural networks 5(3) : 342-353
Kelly, S. E. (1996) : Gibbs phenomenon for wavelets. Appl. Comput. Harm. Anal, vol 3: 72-81
Krim, H. and C.Schick (1999) : Minimax description length for signal denoising and optimized representation. IEEE Transactions on Information Theory, 45(3) : 898-908
Kugarajah, T. and Q.Zhang (1995) ; Multiresolution Wavelet Frames. IEEE Trans, on Neural Networks, 6(6) : 1552-1557
Kovacevic, B.D., M. Durovic.S.T. Glavaski (1992) : On robust Kalman filtering. Int. J. Contr, vol 13: 547-562
Krishnakumar, K. (1989) : Micro-genetic algorithms for stationary and nonstationary function optimation, In Proc. SPIE on Intell. Cont. Adapt. Syst, 1196,289-296
Kulkarni, S.R and P. J. Ramadge (1996) : Multiple-model estimation with variable structure. IEEE Trans, on Automatic Control, 41(11) : 1594-1604
Kuokka, D.R(1995) : Communication infrastructure for concurrent engineering. Artificial Intelligent for Engineering Design, vol 9: 283-297
Lou Yiming, Song Zhihuan,Li Ping (1999) : Estimation and modeling of cross-directional basis weight on paper machines using multiresolution
analysis. In: Han-Fu Chen, Dai-Zhan Cheng and Ji-Feng Zhang, eds 14th world congress of IFAC, Beijing,Pergamon,vol.N,271-276
Lee, J.H, Z. Yu (1998) : Tuning of model predictive controllers for robust performance. Computers and Chenmical Engineering, 18(1) :15-37
Lee and Barlett (1995) : Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes. Neural computation, vol 7: 1040-1053
Lee, J.H, Chikkula (1995) : Improving computatinoal efficient of model predictive control algorithm using wavelet transformation. Int J control, 61(4) :859-883
Lang Hong, Guanrong Chen, and K.C. Charles (1998) : A filter bank based kalman filtering technique for wavelet estimation and decomposition of random signal. IEEE Transaction on circles and systems-II: Analog and digital signal processing, 45(2) : 237-241
Lang Hong (1994) : Multiresolutional Distribution Filtering. IEEE Transaction on Automatic control, 39(4) : 853-856
Lu,Y. et al (1992) : Reasoning about edges in scale space. IEEE Trans on PAMI, 14(4) :450-468
Lane, D.M, A. G. Mcfadzean (1994) : Distribute problem solving and real-time mechanisms in robot architectures. Engineering Application of Artificial Intelligent,7(2) : 105-117
Leonardo, M.R. (1999) : Unification of neural and wavelet networks and fuzzy system. IEEE Transaction on neural networks, 10(4) : 801-814
Mark Hansen and Bin Yu (2000) : Wavelet thresholding via MDL for natural Images. IEEE Tranctions on Information theory, 46(5) : 1778-1788
Martin Vetterli, and Cormac Herley (1992) : wavelet and filters banks: theory and design. IEEE transaction on signal processing, 40(9) : 2207-2228
Michael Nikolaou and Premkiran Vuthandam (1998) : FIR model identification: parsimony through kernel compression with wavelets. AICHE Journal, 44(1) : 141-150
Mohaned, N.N. and B.R. Bakshi (1999) : On-line multiscale filtering of random and gross errors without process models. AICHE Journal, 45 (5) : 1041-1058
Mallat, S and W.L. Hwang (1992) : Singularity detection and processing with
Wavelets. IEEE Transactions on Information Theory, 38(2) :617-643
Masreliez, C. J and R.D. Msrtin (1977) : Robust bayesian estimation for linear model and robustifying the Kalman filter. IEEE Trans on automatic control, vol22:361-371
Mallat,S.G (1989) : A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal. Machine Intell, PAMI-11,674-693
Mallat, S.G And W.L. Hwang (1991) : Singularity detection and processing with wavelets. IEEE Trans. on Inform. Theory, 38(2) : 617-636
Mallat, S.G and Z.F. Zhang (1993) : Matching pursuits with time-frequency dictionaries. IEEE Trans. on Signal Processing, vol 41: 3397-3415
Miao, X.A. (1992) : Normative-Descriptive approach to hierarchical team resource allocation. IEEE Trans. on Sys. Man And Cyb, 32(3) : 62-68
Macvoy, T.J. (1992) : Contemplative stance for chemical process. Automatica, 18(2) : 441-442
Narendra, K. S. and K. Parthasarathy (1990) : Identification and control of dynamical systems using neural networks. IEEE Trans. on Neural Networks, vol 1: 4-27
Narendra, K. S. and J. Balakrishnan (1995) : Adaptation and learning using multiple models, swithching and tuning. IEEE Contr Syst Mag, 15(3) :37-51
Narendra, K. S. and J. Balakrishnan (1995) : Adaptive control using multiple models. IEEE Trans on Automatic Control, 42(2) : 171-187
Neal, C.G., and L.W. Gary (1981) : A theoretical analysis of the properties of median filters. IEEE Transaction on Acoustics, speech, and signal processing, 29(6) : 1136-1140
Neumann, M., and R. Von Sachs (1995) : Wavelet thresholding: beyond the gaussian iid. situation, Lecture Notes in Statistics, Springer-Verlag, 103
Narendra, K.S. (1995) : Adaptation and learning using multiple models switching and tuning. IEEE Control Systems Magazine, 15(3) : 37-51
Pitas, I. and A,N. Venetsanopoulos (1991) : Adaptive filters based on order statistics. IEEE Trans. Signal Processing, 39,518-512
Paul, P. (1999) : Robust huber adaptive filter. IEEE Trans. on Signal Processing, 47(4) : 1129-1133
Partzen, E.(1962) : On estimation of a probability density function and mode. Ann. Math. Stalls, vol 33 : 1065-1076
Palavajjhala, S. et al. (1996) : Process identification using discrete wavelet transforms design of prefilters. AIChE Journal, 42(3) : 777-790
Rostein, H. and S. Raz (1995) : Gabor transform approximation of time-varying systems. Proceedings of the 1995 Americal Control Conference, pp:2424-2429
Rissanen, J. (1978) : Modeling By shortest Data Description. Automatica, vol 14: 465-471
Ronald, A.D, B.Jawerth, and J.L. Bradly (1992) : Image compression through wavelet transform coding. IEEE Transactions on Information Theory, 38(2) :719-746
Rosenblatt, M. (1956) : Remarks on some nonparametric estimates of a density function. Ann. Math. Statist, vol 27: 832-837
Safavi, A.A and J.A. Romagnoli (1997) : Comments on ' Nonlinear Black Box Modeling in system identification: A unified overview'. Automatica, 33(6) : 1197-1198
Sureshbabu, N. And J.A. Farrell (1999) : Wavelet-Based system identification for nonlinear control. IEEE Trans. on Automatic Control, 44(2) : 412-417
Safavi, A. A., J.Chen and J.A. Romagnoli (1997) : Wavelet based density estimation and application to process monitoring. AICHE J, 43(5) : 1227-1241
Safavi, A. A., D. Wang and J.A. Romagnoli (1999) : non-linear system identification using wavelet based on adaptive robust M-estimations, IFAC 14~(th) Trienninal world congress, Beijing.P.R. China, pp: 181-185
Schrama, R. J. P (1992) : Accurate identification for control: The necessity of an iterative schema. IEEE Trans. on Automatic Control, 37(7) : 991-994
SjOberg, J. et.al (1995) : Nonlinear black-box modeling in system identifications unified overview. Mtomatica, 31(12) : 1691-1724
Saridis, G.N (1987) : Knowledge, Implementation, Structure of intelligent control system, Pro of IEEE Int Symp on Intelligent Control
Thompson, M.L, M.A. Kramer (1994) : Modeling chemical process using prior
knowledge and neural networks. AICHE Journal, 40(8) : 1328-1340
Tetsuya Tabaru and Seiichi Shin (2000) : Improvement of wavelet based on dead time measurement method, Proceedings of the 3~(rd) Asian Control Conference, pp: 1929-1934
Tsatsanis, M. K, G.B. Giannakis (1993) : Time-Varying system identification and model validation using wavelets. IEEE Trans on Signal Processing, 41(12) : 3512-3523
Van den Hof, P. M. J. and R. J. P.Schrama (1995) : Identification and Control-Closed-loop Issues. Automatica, 31(12) : 1751-1770
Vapnik and Chervonenkis (1979) : On the uniform convergence of relative frequencies of events to their probabilities. Theory Pro. Appl, vol 16: 264-280
Valient, L.G (1984) : A Theory of the learnable. commun. ACM, vol 27: 1134-1143
Walter, G. G (1995) : A sampling theorem for wavelet subspace. IEEE Trans. on information theory, 38(2) :881-884
Witkin.A (1983) : Scale-space filtering, in proc.int.Joint conf.Artifical intell. pp.1019-1022
Wickerhauser, M. V. (1994) : Adapted wavelet analysis from theory to software. A.K. Peters
Wu X.C. and A. cinar (1996) : An adaptive robust M-estimation for nonparametric nonlinear system identification. J.Proc.contr, 6(4) : 233-239
Wolfgang, H., G. Kerkyacharian, D.Picard, and A. Tsybakow (1998) : Wavelets, approximation and statistical applications, Springer lecture note in statistics, Springer, New York
Wolpert, D. H. (1992) : Stacked Generalization. Neural Networks, 5(2) : 241-245
Wim Sweldens (1996) : Wavelets: what next? Proceedings of the IEEE, 84(4) : 680-685
Xu.Y, et al (1992) : Wavelet transform domain filters: a spatially selective noise filteration technique. IEEE Trans on IP, 13(6) :747-757
Yao Liang and W.Edward (1997) : Multiresolution learning paradigm and signal
prediction. IEEE Trans. Signal Processing, 45(11): 2858-2864
Yang, Z. J, and S. Sagata (1997): System impulse response identification using a multiresolution neural network. Automatica, 33(7): 1345-1350
Zhang, Q. and A. Benveniste (1992): Wavelet network. IEEE Trans. Neural Networks, 8(3): 889-898
Zhang, Q (1997): Using Wavelet Network in Nonparametric Estimation. IEEE Trans. Neural Networks, 8(2): 227-236
Zygmunt Hasiewicz (1999): Hammerstein system identification by the hear multiresolution approximation. Int. J Adapt. Control Process, vol 13, 691-717
Zhao, H.P. and B.Joseph (2000): wavelet-based identification of fast linear time-varying systems using function space methods, Proceedings of the American control conference chicago, illinois. June
Zhang, J., G.G. Walter and W.N. Lee (1995). Wavelet neural network for Function Learning. IEEE Trans. On Signal Processing, vol 43: 1485-1497
Zeljko, D.M., and B.D. Kovacevic (1999): Robust estimation with unknown noise statistics. IEEE Trans. on AC, 44(6): 1292-1296
宋执环(1997)：小波分析在工业过程辨识与控制中的应用研究．浙江大学博士论文
马伯文，曾洪(1990)：控制催化裂化装置粗汽油干点的数学模型．石油炼制，4：67—68．
王钦友，朱筠(1998)：基于Harr函数的分布参数系统辨识．信息与控制，27(5)：326—330．
李海青 黄志尧等(2000)：软测量技术原理及应用，北京：化学工业出版社
王行愚(1994)：系统辨识的若干新方向．控制理论与应用，11(1)：319—321
戴汝为(1991)：从定性到定量的综合集成技术，模式识别与人工智能，14：5-10
张贤达(1999)：现代信号处理，北京：清华大学出版社
杨福生(2000)：小波变换的工程分析与应用，北京：科学出版社
赵松年 熊小芸(1997)：子波变换与子波分析，北京：电子工业出版社
周江文(1989)：经典误差理论与抗差估计．测绘学报，18(2)：36-43
王海清(2000)：工业过程监测：基于小波和统计学的方法．浙江大学博士学位论文
高惠旋(1995)：统计计算，北京：北京大学出版社
张铃，张钹(1998)：前向神经网络设计问题的回顾与探索．计算机工程与科学，20(4)：1-10
方崇智(1988)：系统辨识，北京：清华大学出版社
王惠文(1999)：偏最小二乘回归方法及其应用，北京：国防工业出版社
吴耀军，陶宝祺，袁慎芳(1996)：样条小波网络．模式识别与人工智能，9(3)：228-233
吕立华(1999)：电站钢球磨煤机智能控制系统的研究．浙江大学工业控制技术研究所论文报告
仇林庆，吕立华等(2000)：钢球磨煤机出力测量．电站辅机，vol4，pp：34-36
吕立华，韩学军(1996)：FSSS锅炉炉膛安全保护系统的研制．中国仪器仪表学会学术年会论文集，pp：32-37
李祖枢，徐鸣，周其鉴(1990)：一种新型的仿人智能控制器．自动化学报，16(6)：503-509
范从振(1992)：锅炉原理，水利电力出版设，北京，1992
陈哲，冯天瑾(2000)：小波分析与神经网络结合的研究进展．电子科学学刊，22(3)：496-504
王群仙，李少远(2000)：小波分析及其在控制中的应用．控制与决策，15(4)：385-389
李化，岳刚(2000)：基于自适应小波网络的汽轮发电机组振动故障诊断方法的研究．电工技术学报．15(3)：57-60
王群仙，陈培强(1999)：基于小波网络动态补偿的广义预测控制器．自动化学报，25(5)：-701-704
李向武，韦岗(1998)：基于小波网络的动态系统辨识方法及应用．控制理论与应用，15(4)：494-500
钱峻，邵惠鹤(2000)：一种小波神经网络的在线建模和校正算法．模式识别与人工智能 13(1)：16-21
赵众，将慰孙(2000)：基于小波变换的工况区域识别．清华大学学报：自科版．40(1):91-94
叶昊，张永光(1998)：一种基于小波变换的导弹运输车辆故障诊断方法．自动化学报，24(3)：301-306．
李承暧，谭祖耀(1998)：新加坡国立大学的小波研究．世界科技研究与发展，20(1)：85-92
汤笑笑，李介谷(1998)：基于小波神经网络的系统辨识方法．信息与控制，27(4)：277-282
高协平，张钹(1998)：区间小波神经网络-理论与实现．软件学报，9(3)：217-221
刘山，吴铁军(1997)：一种基于小波逼近的稳定直接自适应控制算法．自动化学报．23(5)：636-640
徐长江，宋文忠(1997a)：基于小波变换估计传递函数．自动化学报，23(6)：835-838
徐长江，宋文忠(1997b)：基于小波变换的分频集员辨识．控制与决策，12(1)：48-52
李银国，张帮礼(1997)：小波神经网络及其结构设计方法．模式识别与人工智能．10(3)：197-205
王真理，李衍达(1995)：基于小波的信号部分重构与插值，清华大学学报：自科版，35(5)：67-72．
吴铁军(1994)：模型失配下故障诊断的小波分析．浙江大学学报，工业过程模型化及控制专辑，29：8-28．
钱峻，邵惠鹤(2000)：一种小波神经网络的在线建模和校正算法．模式识别与人工智能，13(1)：16-21
方浩，薛培鼎(2000)：系统辨识中小波基模型的优化．模式识别与人工智能，13(1)：60-63
袁廷奇(2000)：基于Haar小波变换的持续时间系统参数辨识．电气传动自动化，22(3)：22-23
张拥军 赵光宙(2000)：基于小波分解的时变系统辨识方法研究．浙江大学学报：工学版，034(005)：0541-0543
吕柏权，李天锋(1998)：一种基于小波网络的故障检测方法．控制理论与应用，15(5)：802-805
黄琳，秦化淑，郑应平，郑大钟(1993)：复杂控制系统理论：构想与前景，自动化学报，19(2)：129-136
钱学森(1991)：再谈开放的复杂巨系统．模式识别与人工智能，4(1)：1-4
冯纯伯(1994)：复杂工业生产过程的控制问题．控制理论与应用，11(1)：303-305
高为炳，霍伟(1994)：控制理论的发展与现状．控制理论与应用，11(1)：99—102
张钹 传统人工智能与控制技术的结合．控制理论与应用，11(1)：247—250
李人厚(1994)：复杂大系统的集成控制．控制理论与应用，11(1)：222—225
朱全民(1994)：非线性系统辨识．控制理论与应用，11(1)：661—651
李士勇(2000)：复杂系统、非线性科学与智能控制理论．计算机自动测量与控制，8(4)：1-3
徐道一(1999)：复杂性科学：21世纪的科学，科学时报，1999-10-23