多传感信息建模与动态校正方法研究
|
摘要
实时、高准确度建模和动态校正理论方法是实现制造工业过程的在线、高准确度测量的关键,这对先进制造、仪器科学技术发展具有重要学术价值和实际意义。论文以“多传感信息建模与动态校正方法研究”为题,系统地研究多传感信息预处理方法、多传感信息建模解耦和预测补偿方法,并进一步进行网络化检测系统研制和相关实验与初步应用试验。论文得到教育部产学研结合项目(2007A090302039)和广州市科技计划项目(2005Z3-D0341)资助。
论文对多传感信息预处理与建模校正方法的国内外研究进展和研究热点进行分析,确定论文将利用PLS和NPLS的相关分析、数据简化和多元回归能力,实现传感信息预处理与非线性建模,并结合时间序列分析方法、曲线拟合建模方法和小波多尺度分析方法,开展可在线快速计算的传感信息解耦与动态预测补偿方法研究。主要工作包括:
①研究一种基于多项式外模型-内模型NPLS的多传感信息预处理与建模方法。在多项式外模型-内模型NPLS建模前端,引入基于PLS的预处理实现变量筛选,可使多项式外模型-内模型NPLS建模方法更有实用意义;
②研究基于变量投影重要性-PLS回归系数的多传感信息变量筛选方法。该方法综合考虑变量投影重要性VIP指标、PLS回归系数对自变量的解释作用,有别于以往单一VIP指标作为变量筛选条件易出现误筛的现象。并提出以PLS回归模型拟合误差增量ΔE l作为变量筛选指标,无需逐个地考察每个自变量的重要性,具有计算量少的特点;
③研究一种基于多项式外模型-内模型NPLS的双层非线性回归建模方法。该模型很好地表达了反应变量与解释变量之间、解释潜变量和反应潜变量之间以及反应变量相互之间的非线性关系,模型显式稳健,较好地解决了单独内外模型NPLS方法在应用中难于确定非线性项的问题;
④提出一种简便的多传感信息尺度特征估计方法。该方法对所有传感信息仅进行一次N ( N≥6)尺度分解,求得分辨误差εij和分辨误差阈值ξ,进而完成多传感信息量的尺度特征估计,过程相对比较简单;
⑤提出一种基于尺度逼近的多传感信息自适应插值解耦方法。根据各传感信息分辨级和在预估准确度目标下确定的分辩阈值δ,确定不同插值方法,完成多传感信息解耦计算;
⑥提出一种提高传感动态性能的基于小波计算的传感信息动态预测模型。模型由多分辨近似树原理,利用àtrous算法进行在线小波分解计算,借助小波分析的低通滤波效应,有效抑制噪声干扰,应用基于滑动窗口的多项式预测算法SWPM和基于AR预测模型的并行Kalman递推估计算法REPK算法,分别对平滑层、分辩层信息进行动态预测,有效地利用各分解层信息特点,提高传感系统的动态性能;
⑦系统研究REPK的实现算法和滚动混合式预测算法。REPK算法使用两个Kalman滤波器,交替进行AR模型参数的递推辨识与时变数据中真实信号的最优估计,能根据测量数据的最新分辨信息d j ,t实时修正AR模型参数进行预测,具有良好的计算一致性和收敛性,可推广应用到其它平稳时间序列信号的预测估计中;所提出的滚动混合式预测算法,能够克服长延迟传感信息预测中直接多步预测间隔时间过长问题,将一次长时间预测,分解为若干次直接多步预测,由实测数据开始,用前一次预测得到的数据实现后一次预测模型参数的滚动修正,使得最终预测信息,是由实测数据滚动修正预测获得的,降低预测误差。
⑧结合检测通用化、智能化和网络化要求,设计一种基于嵌入式智能检测节点的网络化检测系统结构模型。研制用于多传感网络化检测的嵌入式智能检测节点,节点采用DSP和ARM微处理器为核心芯片,将所有传感量转换为频率信号,提高信号的抗干扰能力;用ARM的嵌入式微型因特网互联技术进行通信接口设计,在uClinux操作系统中引入IPv6通信模式,提高通信的安全性、可靠性和可扩展性。并讨论网络检测平台的软件结构与运行机制、基于XML的跨平台数据交换技术、基于XML数据的检测平台实时数据库技术等几个关键技术的解决方法。
论文还开展相关仿真实验及应用试验,仿真结果表明,基于多项式外模型-内模型NPLS的多传感信息预处理与建模方法,可在少用拟合自变量的情况下,提高预测准确度(分别提高56.2%和24.7%);基于尺度逼近的多传感信息自适应插值解耦方法在预估准确度目标θ为0.1%下,通过分辩阈值δ计算,取δ=2-4,解耦时间50.4 ms,该方法与神经网络解耦方法相比,具有通用性好、收敛性好、运算速度较快等特点;基于小波计算的传感信息动态预测补偿方法,利用小波快速计算算法进行一次分解的时间为54.3ms,进行一次预测补偿的时间为127.0ms,具有良好的计算实时性;对低延迟传感信息进行直接三步预测时,准确度为0.538%。在发酵液及乙醇精馏中的检测试验初步应用结果表明,应用传感信息解耦与动态预测补偿技术后,使基于嵌入式智能检测节点的网络化检测系统具有较高检测准确度和较好实时性能,液态乙醇浓度的最大检测误差为-1.9%,传感检测响应时间从20s提高到1.3s。这些都表明本论文所研究的理论方法正确性、有效性,成果可推广到其它先进制造过程等应用领域。
Real-time and high precision modeling &dynamic compensation method is a key to realize online and high precision measurement of manufacturing process, it has important academic value and practical significance in promoting the development of advanced manufacture and instrument technology. With the title“Study on Multiple Sensor Information Modeling &Dynamic Compensation”, the thesis systematically studies sensing information preprocessing, decoupling, prediction compensation method, and farther develops a networking measurement system, carries through correlative experiments and primary application. The thesis is supported by Guangzhou Science and Technology Planning Project (No.2005Z3-D0341) and Industry-Academy-Research Project of Education Ministry (2007A090302039).
The thesis first analyzes the domestic and international researches on sensing information preprocessing &modeling method. It confirms the thesis will use correlation analysis, data reduction and multiple regressions ability of PLS and NPLS method to realize sensing information preprocessing and nolinar-modeling. And then the thesis combines time series analysis method ,curve fitting and wavelet-multiscale method together to develop online-rapid decoupling and prediction compensation method of sensing information The main work includes the following parts:
I. It studies a NPLS preprocessing and modeling method based on outer-inner polynomial model. Before outer-inner polynomial NPLS modeling, importing variable selection based PLS can make the outer-inner polynomial NPLS modeling method have more practical significance.
II. It studies a multiple sensor information variable selection method based on VIP-PLS regression coefficient. The method considers synthetically VIP index and PLS regression coefficient interpretative action on independent variables, and differing from the former method with single VIP filtration index, it doesn’t take place the phenomena of wrong filtration easily. And the method brings forward using error incrementΔEl of PLS model as variable filtering index, it needn’t review each independent variable’s importance, and it has virtue of small calculation work.
III. It studies a double non-linearization PLS regression modeling method based on outer-inner polynomial model. The built model is explicit, steady and can express non-linear relation between explanatory variables and responsive variables, explanatorily latent variables and responsively latent variables, and among responsive variables commendably, it solves problem about the nonlinear terms is hard to confirmed in modeling process of outer polynomial NPLS model.
IV. It brings forward a handy scale estimation method of multi-sensing information. The method just processes an N ( N≥6) scale decomposing for all sensing information, and works out resolution errorεij and resolution error thresholdξ, then it can fulfill scale estimation of multi-sensing information variables, its process is relatively simple.
V. It brings forward a adaptive interpolation decoupling method of multi-sensing information based on scale approximation. The method select different interpolation method to fulfill decoupling calculation of multi-sensing information by resolution of each sensing information and resolution thresholdδcalculated under preestimating precision target.
VI. It brings forward a dynamic prediction model of sensing information based on wavelet calculation to improve dynamic sensing characteristic. Based on multi-resolution approximation tree principle, the model usesàtrous arithmetic to process online wavelet-decomposing calculation, it can restrain noise disturbance effectively in virtue of low-pass filtering effect of wavelet analysis. The model uses Sliding Window Polynomial Model (SWPM) arithmetic, and Recursive Estimator based on Parallel Kalman (REPK) arithmetic of AR prediction model to dynamically predict scale information and detail information respectively, it can make use of each decomposed information’s characteristic effectively and improve dynamic performance of sensing system.
VII. It systemically studies REPK arithmetic and composite-scroll prediction arithmetic. REPK arithmetic uses two Kalman filter to recursively identify parameters of AR model and optimally estimate true signal in time-varying data, it uses fresh resolution information d j ,tof measurement data to real-time amend parameters of AR model and predict, the arithmetic has good calculation consistency and convergence, and can be extended to prediction of other stationary time series signal. The proposed composite-scroll prediction arithmetic can overcome long interval problem in direct multi-step prediction method about prediction of long delay sensing information, it divides a long-time prediction to several direct multi-step prediction, and it starts from measured data, uses forward prediction data to amend parameters of afterward prediction model, and the final prediction data is scroll-amendatorily calculated from measured data, it decreases prediction error.
VIII. Combined with request of measurement generalization, intelligentization and networking, it designs a networking structure model of measurement system based on embedded intelligent agent. The intelligent agent is used for networking measurement of multi-sensing information, it uses DSP and ARM as kernel chip, transforms all sensing information to frequency signal, and it increases antijamming ability of signal. The agent realizes the design of network communication by ARM embedded mini-internet technology, imports IPv6 communication mode in uClinux operation system, and it increases security, reliability and expansibility of communication. It also discusses resolvents of several pivotal technology about soft structure and operational mechanism, cross platform exchange technology based on XML, and real-time database technology of measurement platform based on XML data.
The thesis also carries through correlative emulational experiments and applicational trial. The emulational result shows the NPLS preprocessing and modeling method based on outer-inner polynomial model can improve predictional precision(improves 56.2% and 24.7% respectively) with less fitting independent variables. After resolution thresholdδis calculatedδ=2-4 under preestimating precision targetθ=0.1%, the decoupling time of proposed adaptive interpolation decoupling method is 50.4 ms, the decoupling method has good generalization, convergence and rapid calculation speed compared with NN decoupling method. Useing wavelet rapid calculation arithmetic, the dynamic prediction model of sensing information based on wavelet calculation processes one time decomposition need 54.3ms, one time prediction compensation need 127.0ms, it has good real-time characteristic, and its precision is 0.538% when it uses direct three-step prediction method for low delay sensing information. After the decoupling and dynamic prediction compensation technology is used, the primary application result in measurement of ferment liquid and ethanol rectification shows the networking measurement system based on embedded intelligent agent has high measurement precision and good real-time characteristic, the maximal measurement error of liquid ethanol concentration is±1.9%, and improves responsing time of ethanol sensors from 20s to 1.3s. these show the researched theory and methods are correctness and validity, and can be extended to other advanced manufacturing processes.
论文对多传感信息预处理与建模校正方法的国内外研究进展和研究热点进行分析,确定论文将利用PLS和NPLS的相关分析、数据简化和多元回归能力,实现传感信息预处理与非线性建模,并结合时间序列分析方法、曲线拟合建模方法和小波多尺度分析方法,开展可在线快速计算的传感信息解耦与动态预测补偿方法研究。主要工作包括:
①研究一种基于多项式外模型-内模型NPLS的多传感信息预处理与建模方法。在多项式外模型-内模型NPLS建模前端,引入基于PLS的预处理实现变量筛选,可使多项式外模型-内模型NPLS建模方法更有实用意义;
②研究基于变量投影重要性-PLS回归系数的多传感信息变量筛选方法。该方法综合考虑变量投影重要性VIP指标、PLS回归系数对自变量的解释作用,有别于以往单一VIP指标作为变量筛选条件易出现误筛的现象。并提出以PLS回归模型拟合误差增量ΔE l作为变量筛选指标,无需逐个地考察每个自变量的重要性,具有计算量少的特点;
③研究一种基于多项式外模型-内模型NPLS的双层非线性回归建模方法。该模型很好地表达了反应变量与解释变量之间、解释潜变量和反应潜变量之间以及反应变量相互之间的非线性关系,模型显式稳健,较好地解决了单独内外模型NPLS方法在应用中难于确定非线性项的问题;
④提出一种简便的多传感信息尺度特征估计方法。该方法对所有传感信息仅进行一次N ( N≥6)尺度分解,求得分辨误差εij和分辨误差阈值ξ,进而完成多传感信息量的尺度特征估计,过程相对比较简单;
⑤提出一种基于尺度逼近的多传感信息自适应插值解耦方法。根据各传感信息分辨级和在预估准确度目标下确定的分辩阈值δ,确定不同插值方法,完成多传感信息解耦计算;
⑥提出一种提高传感动态性能的基于小波计算的传感信息动态预测模型。模型由多分辨近似树原理,利用àtrous算法进行在线小波分解计算,借助小波分析的低通滤波效应,有效抑制噪声干扰,应用基于滑动窗口的多项式预测算法SWPM和基于AR预测模型的并行Kalman递推估计算法REPK算法,分别对平滑层、分辩层信息进行动态预测,有效地利用各分解层信息特点,提高传感系统的动态性能;
⑦系统研究REPK的实现算法和滚动混合式预测算法。REPK算法使用两个Kalman滤波器,交替进行AR模型参数的递推辨识与时变数据中真实信号的最优估计,能根据测量数据的最新分辨信息d j ,t实时修正AR模型参数进行预测,具有良好的计算一致性和收敛性,可推广应用到其它平稳时间序列信号的预测估计中;所提出的滚动混合式预测算法,能够克服长延迟传感信息预测中直接多步预测间隔时间过长问题,将一次长时间预测,分解为若干次直接多步预测,由实测数据开始,用前一次预测得到的数据实现后一次预测模型参数的滚动修正,使得最终预测信息,是由实测数据滚动修正预测获得的,降低预测误差。
⑧结合检测通用化、智能化和网络化要求,设计一种基于嵌入式智能检测节点的网络化检测系统结构模型。研制用于多传感网络化检测的嵌入式智能检测节点,节点采用DSP和ARM微处理器为核心芯片,将所有传感量转换为频率信号,提高信号的抗干扰能力;用ARM的嵌入式微型因特网互联技术进行通信接口设计,在uClinux操作系统中引入IPv6通信模式,提高通信的安全性、可靠性和可扩展性。并讨论网络检测平台的软件结构与运行机制、基于XML的跨平台数据交换技术、基于XML数据的检测平台实时数据库技术等几个关键技术的解决方法。
论文还开展相关仿真实验及应用试验,仿真结果表明,基于多项式外模型-内模型NPLS的多传感信息预处理与建模方法,可在少用拟合自变量的情况下,提高预测准确度(分别提高56.2%和24.7%);基于尺度逼近的多传感信息自适应插值解耦方法在预估准确度目标θ为0.1%下,通过分辩阈值δ计算,取δ=2-4,解耦时间50.4 ms,该方法与神经网络解耦方法相比,具有通用性好、收敛性好、运算速度较快等特点;基于小波计算的传感信息动态预测补偿方法,利用小波快速计算算法进行一次分解的时间为54.3ms,进行一次预测补偿的时间为127.0ms,具有良好的计算实时性;对低延迟传感信息进行直接三步预测时,准确度为0.538%。在发酵液及乙醇精馏中的检测试验初步应用结果表明,应用传感信息解耦与动态预测补偿技术后,使基于嵌入式智能检测节点的网络化检测系统具有较高检测准确度和较好实时性能,液态乙醇浓度的最大检测误差为-1.9%,传感检测响应时间从20s提高到1.3s。这些都表明本论文所研究的理论方法正确性、有效性,成果可推广到其它先进制造过程等应用领域。
Real-time and high precision modeling &dynamic compensation method is a key to realize online and high precision measurement of manufacturing process, it has important academic value and practical significance in promoting the development of advanced manufacture and instrument technology. With the title“Study on Multiple Sensor Information Modeling &Dynamic Compensation”, the thesis systematically studies sensing information preprocessing, decoupling, prediction compensation method, and farther develops a networking measurement system, carries through correlative experiments and primary application. The thesis is supported by Guangzhou Science and Technology Planning Project (No.2005Z3-D0341) and Industry-Academy-Research Project of Education Ministry (2007A090302039).
The thesis first analyzes the domestic and international researches on sensing information preprocessing &modeling method. It confirms the thesis will use correlation analysis, data reduction and multiple regressions ability of PLS and NPLS method to realize sensing information preprocessing and nolinar-modeling. And then the thesis combines time series analysis method ,curve fitting and wavelet-multiscale method together to develop online-rapid decoupling and prediction compensation method of sensing information The main work includes the following parts:
I. It studies a NPLS preprocessing and modeling method based on outer-inner polynomial model. Before outer-inner polynomial NPLS modeling, importing variable selection based PLS can make the outer-inner polynomial NPLS modeling method have more practical significance.
II. It studies a multiple sensor information variable selection method based on VIP-PLS regression coefficient. The method considers synthetically VIP index and PLS regression coefficient interpretative action on independent variables, and differing from the former method with single VIP filtration index, it doesn’t take place the phenomena of wrong filtration easily. And the method brings forward using error incrementΔEl of PLS model as variable filtering index, it needn’t review each independent variable’s importance, and it has virtue of small calculation work.
III. It studies a double non-linearization PLS regression modeling method based on outer-inner polynomial model. The built model is explicit, steady and can express non-linear relation between explanatory variables and responsive variables, explanatorily latent variables and responsively latent variables, and among responsive variables commendably, it solves problem about the nonlinear terms is hard to confirmed in modeling process of outer polynomial NPLS model.
IV. It brings forward a handy scale estimation method of multi-sensing information. The method just processes an N ( N≥6) scale decomposing for all sensing information, and works out resolution errorεij and resolution error thresholdξ, then it can fulfill scale estimation of multi-sensing information variables, its process is relatively simple.
V. It brings forward a adaptive interpolation decoupling method of multi-sensing information based on scale approximation. The method select different interpolation method to fulfill decoupling calculation of multi-sensing information by resolution of each sensing information and resolution thresholdδcalculated under preestimating precision target.
VI. It brings forward a dynamic prediction model of sensing information based on wavelet calculation to improve dynamic sensing characteristic. Based on multi-resolution approximation tree principle, the model usesàtrous arithmetic to process online wavelet-decomposing calculation, it can restrain noise disturbance effectively in virtue of low-pass filtering effect of wavelet analysis. The model uses Sliding Window Polynomial Model (SWPM) arithmetic, and Recursive Estimator based on Parallel Kalman (REPK) arithmetic of AR prediction model to dynamically predict scale information and detail information respectively, it can make use of each decomposed information’s characteristic effectively and improve dynamic performance of sensing system.
VII. It systemically studies REPK arithmetic and composite-scroll prediction arithmetic. REPK arithmetic uses two Kalman filter to recursively identify parameters of AR model and optimally estimate true signal in time-varying data, it uses fresh resolution information d j ,tof measurement data to real-time amend parameters of AR model and predict, the arithmetic has good calculation consistency and convergence, and can be extended to prediction of other stationary time series signal. The proposed composite-scroll prediction arithmetic can overcome long interval problem in direct multi-step prediction method about prediction of long delay sensing information, it divides a long-time prediction to several direct multi-step prediction, and it starts from measured data, uses forward prediction data to amend parameters of afterward prediction model, and the final prediction data is scroll-amendatorily calculated from measured data, it decreases prediction error.
VIII. Combined with request of measurement generalization, intelligentization and networking, it designs a networking structure model of measurement system based on embedded intelligent agent. The intelligent agent is used for networking measurement of multi-sensing information, it uses DSP and ARM as kernel chip, transforms all sensing information to frequency signal, and it increases antijamming ability of signal. The agent realizes the design of network communication by ARM embedded mini-internet technology, imports IPv6 communication mode in uClinux operation system, and it increases security, reliability and expansibility of communication. It also discusses resolvents of several pivotal technology about soft structure and operational mechanism, cross platform exchange technology based on XML, and real-time database technology of measurement platform based on XML data.
The thesis also carries through correlative emulational experiments and applicational trial. The emulational result shows the NPLS preprocessing and modeling method based on outer-inner polynomial model can improve predictional precision(improves 56.2% and 24.7% respectively) with less fitting independent variables. After resolution thresholdδis calculatedδ=2-4 under preestimating precision targetθ=0.1%, the decoupling time of proposed adaptive interpolation decoupling method is 50.4 ms, the decoupling method has good generalization, convergence and rapid calculation speed compared with NN decoupling method. Useing wavelet rapid calculation arithmetic, the dynamic prediction model of sensing information based on wavelet calculation processes one time decomposition need 54.3ms, one time prediction compensation need 127.0ms, it has good real-time characteristic, and its precision is 0.538% when it uses direct three-step prediction method for low delay sensing information. After the decoupling and dynamic prediction compensation technology is used, the primary application result in measurement of ferment liquid and ethanol rectification shows the networking measurement system based on embedded intelligent agent has high measurement precision and good real-time characteristic, the maximal measurement error of liquid ethanol concentration is±1.9%, and improves responsing time of ethanol sensors from 20s to 1.3s. these show the researched theory and methods are correctness and validity, and can be extended to other advanced manufacturing processes.
引文
[1] M. Rudan, P. Ciampolini, M.C. Vecchi et. Sensor Modeling [J]. Sensors Update, 2001,4(1):109-137
[2]中国仪器仪表协会.中国测量控制与仪器仪表中长期科技发展规划建设[R].2004,9
[3]中国仪器仪表协会.现代仪器仪表发展和未来五年我国对仪器仪表市场需求分析报告[R].2004,7
[4] Yanhua Ruan, Lang Hong. Use of the interacting multiple model algorithm with multiple sensors[J]. Mathematical and Computer Modelling, 2006,44(3):332-341
[5] Salahshoor Karim, Kordestani Mojtaba, Khoshro Majid S. Design of online soft sensors based on combined adaptive PCA and RBF neural networks[C]. IEEE Symposium on Computational Intelligence in Control and Automation,2009:89-95
[6] Yang Hai-lan, Cai Yan, Bao Ye-feng, Zhou Yun. Analysis and application of partial least square regression in arc welding process [J]. Central South University of Technology 2005,12(4):453-458
[7] R.Rego a,b,A.Mends. Carbon dioxide/methane gas sensor based on permselectivity of polymeric membranes for biogas monitoring[J]. Sensors and Actuators B.2004,103:2-6
[8] N.Rajabbeigi,B.Elyassi,A.Khodadadi,S.S.Mohajerzadeh,M.Sahimi.A novel minia -turized oxygen sensor with solid-state ceriazirconia reference[J]. Sensors and Actuators B.2004,100:139-142
[9] Gorban A.N., Kégl B.,Wunsch D.C et. Principal Manifolds for Data Visualization and Dimension Reduction [R]. Lecture Notes in Computational Science and Engineering. 2008,Vol.58:340
[10]付克昌.基于结构优化PCA的传感器故障诊断方法及其应用研究[D].浙江大学博士学位论文,2007
[11] Alexandre Perera, Niko Papamichail, Nicolae Barsan. et al. On-Line Novelty Detection by Recursive Dynamic Principal Component Analysis and Gas Sensor Arrays Under Drift Conditions[J]. Sensors Journal.2006,6(3):770-783
[12] N. Rivara, P.B. Dickinson, A.T. Shenton. A transient virtual-AFR sensor using the in-cylinder ion current signal[DB/OL]. http://www.sciencedirect.com/science. 2009,1
[13] T. Katsube, S. Umetani, Liqin Shi and Y. Hasegawa. Sensor Fusion for Taste Sensor and Odor Sensor[J]. Chemical Senses 2005,30(1): 260-264
[14] Geladi P,Kowalski B R.Partial least-squares regression:a tutorial[J]. Analytica Chimica Acta, 1986,185:1-17
[15] Hoskuldsson,A PLS regression methods[J].Chemometrics,1988,2: 211-228
[16] Lorber A,Wangen L E,Kowalski B R.A theoretical foundation for the PLS algorithm[J].Chemometrics,1987,1:19-31
[17] Yang Hai-lan, Cai Yan, Bao Ye-feng, Zhou Yun. Analysis and application of partial least square regression in arc welding process[J].Central South University of Technology 2005,12(4):453-458
[18] J.M.Karthikeya Udayagiri.V.R, Taleb Moazzeni et al. Detection algorithms for the Nano Nose[C]. 19th International Conference on Systems Engineering. 2008:399-404
[19]梁军.轧钢加热炉钢坯加热质量的检测研究(II)[J].传感技术学报. 2003,6(2):117 - 123
[20] D. Wang, R. Srinivasan, J. Liu et al. Data-driven Soft Sensor Approach For Quality Prediction in a Refinery Process[C]. IEEE International Conference on Industrial Informatics, 2006:230-235
[21]蔡艳,杨海澜,许轲,吴毅雄等.短路过渡弧焊过程稳定性在线评价模型设计[J].上海交通大学学报.2005,39(7):1038-1041
[22] Carlos Enrique Carleos Artime , Jes's Angel Baro de la Fuente et al. On-line Estimation of Fresh Milk Composition by means of VIS-NIR Spectrometry and Partial Least Squares Method[C].IEEE International Instrumentation and Measurement Technology Conference,Victoria, Vancouver Island, Canada, 2008,5:1-5
[23] Bin Zhang, Lei Deng, Qiao Gao et al. Fast Discrimination of Chocolate Varieties Using Near Infrared Spectroscopy[C]. Proceedings of the IEEE International Conference on Automation and Logistics.Qingdao, China , 2008,9:730-735
[24]徐惠荣,汪辉君,黄康等. PLS和SMLR建模方法在水蜜桃糖度无损检测中的比较研究[J].光谱学与光谱分析.2008,28(11):2523-2526
[25] Frank Dieterle,Stefan Busche,Günter Gauglitz. Different approaches to multivariate calibration of nonlinear sensor data[J]. Analytical and Bioanalytical Chemistry . 2004,380(3):383-396
[26]李春富.基于数据的软测量建模方法及其应用的研究[D].清华大学工学博士学位论文. 2004
[27] C. Li , H. Ye, G. Wang, J. Zhang, A Recursive Nonlinear PLS Algorithm for Adaptive Nonlinear Process Modeling[J]. Chemical Engineering & Technology. 2005,28(2):141-152
[28] Chunfu Li, Jie Zhang, Guizeng Wang. Batch-to-Batch Optimal Control of Batch Processes Based on Recursively Updated Nonlinear Partial Least Squares Models [J]. Chemical Engineering Communications. 2007, 194:261-279
[29] Haixian Wang, Zilan Hu .Maximum Margin Criterion Embedded Partial Least Square Regression for Linear and Nonlinear Discrimination[C]. Computational Intelligence and Security, 2006 vol.1:33-38
[30] Tenenhaus A., Guillemot V. , Gidrol X.. Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on PLS Regression [J],Computational Biology and Bioinformatics. 2008,8:1
[31] Wo1d S,Kettaneh- Wold N,Skagerberg B.Nonlinear PLS modeling[J],Chemometries and Intelligent Laboratory Systems.1989,7: 53-65
[32] Baffi, G.; Martin, E.B.; Morris, A.J. Non-linear projection to latent structures revisited:the quadratic PLS algorithm[J].Computers and Chemical Engineering. 1999,23(3): 395-411
[33] Wold S, Eriksson L, Sjo¨stro¨m M. PLS in chemistry. In Encyclopedia of Computational Chemistry, ed. Scleyer PVR[DB]. Wiley: New York, 1998; 2006–2016
[34]粱军,汪小勇,王文庆.基于神经网络PLS方法的软测量建模研究[J].浙江大学学报(工学版).2004,38(6),676-681
[35] Tang K,Li T,Combining PLS with GA-GP for QSAR [J],Chemometrics and Intelligent Laboratory Systems.2002,64:55-64
[36]成忠,陈德钊.模糊偏最小二乘及其在药物构效关系中的应用[J].浙江大学学报.2005,39(10):1613-1617
[37] Liqing Di,Zhihua Xiong, Xianhui Yang. Nolinear process modeling and optimization based on multiway kernel partial least squares model [C]. Proceedings of the Winter Simulation Conference, 2008:1645-1651
[38]张琳,张黎明,李燕等.多项式偏最小二乘法对非线性体系红外谱图的分析[J].光谱学与光谱分析.2006,26(4):620-623
[39] Baffi G,Martin EB,Morris AJ.Nonlinear projection to latent structure revisited:the quadr -atic partial least squares (PLS) algorithm [J].Compute Chem.Eng.1999,23 (4):395- 411
[40]于晓栋,黄德先,王雄.差分进化在基于非线性规划的多项式PLS中的应用[J].化工自动化及仪表, 2007, 34(4): 14-17
[41] Piwowar J.M,Ledrew E.F. ARMA time series modelling of remote sensing imagery: a new approach for climate change studies[J]. International Journal of Remote Sensing. 2002,23(24):5225-5248
[42]李捷,刘先省,韩志杰.基于ARMA的无线传感器网络流量预测模型的研究[J].电子与信息学报.2007,29(5):1224-1227
[43] Liu Datong; Peng Yu; Peng Xiyuan. Online Fault Prediction Based on Combined AOSVR and ARMA Models[C],Testing and Diagnosis.2009:1-4
[44] Zhu Wang,Henry L.Gray,Wayne A. Woodward. The Application of the Kalman Filter to Nonstationary Time Series through Time Deformation[J/OL]. https://smu.edu/statistics/ TechReports. 2008,3:1-33
[45]邓自立,郝钢.自校正分布式观测融合Kalman滤波器[J].电子与信息学报,2007,29(6): 1850-1854
[46] Benvensite A, Nikoukhah R,Willsky A. S. Multiscale system theory[J]. In:Proc. 29th IEEE conference on decision and control,Honolulu,HI,1990:2484-2487
[47] Chou K C,Willsky As,Benvensite A. Multiscale recursive estimation, data fusion, and regularization[J]. IEEE trans. On Automatic control,1994,39(3):464-478
[48] Chou K C,Willsky A s,Nikoukhah R. Multiscale system, Kalman filter, and riccati equations[J]. IEEE trans. On Automatic control,1994,39(3):479-492
[49] Basseville M, Benvensite A, Willsky A.S. Multiscale autoregressive process,Part I:Schur-Levinson parametrization[J].IEEE Trans. On signal processing,1992,40(8): 1915-1934
[50] K. Chou, S. A. Golden, A. S. Willsky. Multiresolution stochastic models, data fusion and wavelet transform[C]. Signal Processing, 1993, 34(3): 257-282
[51] L. Hong. Multiresolution distributed filtering[C]. IEEE traps. On Automatic Control, 1994, 39(4):853-856
[52]潘泉,张磊,崔培玲,张洪才.一类动态多尺度系统的最优滤波[J].中国科学(E辑),2004,34(4):433-447
[53]文成林,陈志国,闫莉萍,周东华.基于多速率传感器动态系统的多尺度递归融合估计[J];电子与信息学报.2003,25(3): 306-312
[54] Xiaoli Li, Xin Yao. Multi-scale statistical process monitoring in machining[J]. Industrial Electronics, IEEE Transactions on.2005,52(3):924-927
[55] Truchetet,Frédéric,Laligant,Olivier.Review of industrial applications of wavelet and multi-resolution based signal and image processing [J]. Electron.Imaging, 2008,17(9): 1412 -1430
[56] Rathinam, A,Padmini, S,Ravikumar, V. Application of supervised learning artificial neural networks [CPNN,BPNN] for solving power flow problem[C].ICTES 2007:156-160
[57] Lei Wang, Cheng Shao, Hai Wang, Hong Wu. Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases[J]. Journal of Natural Gas Chemistry, 2006,15(3) : 230-234
[58] Duduku Krishnaiah, D.M. Reddy Prasad, Awang Bono, et al. Application of ultrasonic waves coupled with functional link neural network for estimation of carrageenan concentration[J]. nternational Journal of Physical Sciences.2008, 3 (4):90-96
[59] Cortes C,Vapnic V.Support-vector networks[J].Machine Learning ,1995,20(3):273-297
[60] Abdul Md Mazid, A B M Shawkat Ali. Opto-tactile Sensor for Surface Texture Pattern Identification using Support Vector Machine[C],10th Intl. Conf. on Control, Automation, Robotics and Vision. Hanoi, Vietnam.2008,12:1830-1835
[61] Yonggang Li,Weihua Gui,Chunhua Yang,Zhisheng Chen. Distributed SVMs based soft sensor and its application for high pressure dissolving [C]. Intelligent Control and Automation, 2008: 5611-5615
[62] Vincent Bombardier,Cyril Mazaud,Pascal Lhoste et al. Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system[J]. Computers in Industry.2007, 58(4):355-366
[63]宋国民.多分力车轮力传感器研究及其在汽车道路试验中的应用[D].东南大学博士学位论文.2001
[64] Yinan Zhang,Qingwei Sun, He Quan et al. Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning[J].Journal of Systems Engineering and Electronics.2006,17(3):495-501
[64] S. Jassar, Z. Liao, L. Zhao. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems[DB/OL]. Building and Environment, available on sciencedirect.com,2008.10
[65] J.Holland. Genetic algorithms and classifier systems, Foundations and future directions,Genetic algorithms and their application[C], Proceedings of the second international conference on Genetic algorithms,1987:88-89
[66]芦俊,陈俊杰,颜景平.遗传算法在传感器非线性校正中的应用[J].传感器技术.2003,22(6):56-57,61
[67] Jamaluddin,Hishamuddin,Abd. Samad, M. F. et al. Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification[J]. Journal of Systems and Control Engineering, 2007,221 (7):975-989
[68] Kim, S, Kim, HS. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling[J].Journal of Hydrology,2008,351(3-4): 299-317
[69] Yang Zhangang; Che Yanbo; Cheng, K.W.E. Genetic Algorithm-Based RBF Neural Network Load Forecasting Model[C]. IEEE Power Engineering Society General Meeting, 2007.6:1- 6
[70] Celal Yildiz, Mustafa Turkmen New and very simple CAD models for coplanar waveguide synthesis [J], Microwave and Optical Technology Letters. 2004, 41(1):49-53
[71] Bo Li,Lagrange interpolation and finite element superconvergence[J],Numerical Methods for Partial Differential Equations,2004,24(1):33-59
[72] Krassimira Vlachkova,A Newton-type algorithm for solving an extremal constrained interpolation problem[J].Numerical Linear Algebra with Applications,2000,7(3):133-146
[73] Thomas Kunkle, Multivariate Differences, Polynomials, and Splines[J], Journal of Approximation theory 1996,84:290_314
[74]余跃,冯志刚.分形插值样条的定义以及计算研究[J].工程图学学报,2008,4:86-90
[75]吕春兰,梁伟,杨世儒.高准确度的传感器建模方法及应用[J].传感器技术.2003,22 (5) :3-5
[76]李建利,房建成,盛蔚.MEMS陀螺标度因数误差分析及分段插值补偿[J].北京航空航天大学学报.2007,9:1064-1067
[77] Meng-Fu Wang,F. T. K. Au. Precise integration methods based on Lagrange piecewise interpolation polynomials[DB/OL]. International Journal for Numerical Methods in Engineering.Published Online 2008
[78] Stevan Dubljevic, Panagiotis D. Christofides, Ioannis G. Kevrekidis. Distributed nonlinearcontrol of diffusion-reaction processes[J]. International Journal of Robust and Nonlinear Control. 2004, 14(2):133-156
[79] Jin H.Z., Lu H.,Cho S.K et al. Nonlinear compensation for non-contact electronic joystick with a single hall senso[J].Science, Measurement & Technology, IET.2008,2(1):9-17
[80] D. Labarre, E.Grivel, Y.Berthoumieu, et al. Consistent estimation of autoregressive parameters from noisy observations based on two interacting Kalman filters[J]. Signal processing, 2006,86:2863-2876
[81]文成林.多尺度动态建模理论及其应用[M].北京:科学出版社. 2008:1-20
[82] Shi Jian,Liu Xinggao. Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis[J]. Chinese Journal of Chemical Eingerring. 2005, 13 (6):849-852
[83]林继鹏,刘君华.基于小波的支持向量机算法研究[J].西安交通大学学报.2005,39(8):816-819
[84] Harri N,Teri H,Ari K et al.Evolving the neural network model for forecasting air pollution time series[J]. Engineering Applications of Artificial Intelligence, 2004,17:159-167
[85]谷吉海,姜兴渭,王晓锋等.双向Elman神经网络在卫星电池阵功率预测中的应用研究[J].南京理工大学学报,2004,28(5):405-409
[86]刘清.考虑测量噪声的传感器动态测量误差补偿[J].江苏大学学报,2007,27(2):160-163
[87] FU HuiMin,Progressive autogressive prediction method[J].Joumal of Mechanical Strength,2006, 28(1):34-39
[88]黄永梅,张桐,唐涛等.卡尔曼预测滤波对跟踪传感器延迟补偿的算法研究[J].光电工程,2006,33(6):4-9
[89]王风宇,云晓春,申伟东.基于小波变换的网络流量在线预测模型[J].高技术通讯,2006,16(12):1219-1225
[90] Olivier Renaud, Jean-Luc Starck, Fionn Murtagh. Wavelet-Based Combined Signal Filtering and Prediction[J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS -PART B: CYBERNETICS, 2005,35(6):1241-1251
[91]孙振明,姜兴渭,王晓锋等.a Trous小波在卫星遥测数据递归预测中的应用[J].南京理工大学学报,2004,28(6):606-611
[92] Xu Kejun, Li Cheng. Dynamic Decoupling and compensation methods of multi-axis force sensors[J].IEEE Transactions on Instrumentation and Measure -ment.2000 , 49(5): 935-941
[93] Dongchuan Yu, Qinghao Meng, Jiang Wang, et al. Neural network based left-inverse system dynamic decoupling & compensating method of multi-dimension sensors[J]. American Control Conference, 2005, 6(3): 1727-1732
[94]刘桂雄,李夏妮,周德光,基于多尺度数值计算的传感信息解耦新方法[J].光学精密工程, 2005,13: 164-167
[95]丁明理,梁宏,王祁等.基于小生境遗传算法的多维传感器动态解耦方法[J].传感器技术学报,2006,19(3):667-671
[96] Silvia L.T. Lima et al.PLS Pruning : a new approach to variable seleetion for multivariate calibration based on Hessian matrix of errors[J]. Chemometrics and intelligent Laboratory System.2005.76(1):6-11
[97] Frank Dieterle,Stefan Busche,Günter Gauglitz. Different approaches to multivariate calibration of nonlinear sensor data. Analytical and Bioanalytical Chemistry . 2004, 380(3):383-396
[98]李军会;秦西云;张文娟等.局部偏最小二乘回归建模参数对近红外检测结果的影响研究[J].光谱学与光谱分析.2007,27(2):262-264
[99] Fei Liu,Yong He, Li Wang . Discrimination of Varieties of Yellow Wines by Using Vis /NIR Spectroscopy and PLS-BP Model [C]. Control and Automation, 2007,5: 1492-1495
[100] Jun Chi-Hyuck, Sang-Ho Lee, Hae-Sang Park, Jeong-Hwa Lee. Use of partial least squares regression for variable selection and quality prediction [C]. Computers & Industrial Engineering, 2009:1302-1307
[101]丁光辉. PLS和GA应用于部分有机污染物的QSAR研究[M],大连理工大学博士学位论文.2006
[102]刘桂雄,林绪洪.鱼类超微弱发光的偏最小二乘回归分析与建模[J].华南理工大学学报(自然科学版), 2006, 34(11): 29-32
[103] Yumin He,Xuefeng Chen, Jiawei Xiang,et al. Multiresolut-ion analysis for finite element method using interpolating wavelet and lifting scheme [DB/OL]. Communications in numeric methods in engineering, publised online in Wiley InterScience 2007
[104] Olivier Renaud,Jean-Luc Starck,Fionn Murtagh. Prediction based on a multiscale decomposition[J]. Int.J.Wavelets, Multires. And Inform process, 2003,1(2):217-232
[105]张弦,王宏力.基于最优分解尺度的静态提升小波去噪方法[J].中国电机工程学报,2009,35(3):501-508
[106] Kaew pijit S, Le moigne J, El-Ghazawi T. Automatic reduction of hyperspectral imagery using wavelet spectral analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 863-871
[107]刘桂雄,李夏妮,周德光,基于多维插值补偿其它因素对传感特性影响的数学方法[J].光学精密工程,2004,12(3):258-261
[108]叶廷东,刘桂雄,黄国健,陈铁群.基于多尺度逼近的多维传感信息解耦新方法[J].华南理工大学学报,2009,4:45-49
[109] ERIC Miller, Alan S. Willsky, A Multiscale approach to sensor fusion and the solution of linear inverse problems[J]. Applied and Computational Harmonic Analysis 1995,2:127-147
[110]仲崇权,董西路,张立勇,曹阳.多传感器测量中方差估计[J].数据采集与处理,2003,18(4):412-417
[111] Roal J R, Girja G. Sensor data fusion algorithms using square-root information filtering[J]. IEEE Proc Radar Sonar Naving,2002,149(2):89-96
[112]姜力,刘宏,蔡鹤皋.多维力/力矩传感器静态解耦的研究[J].仪器仪表学报,2004,25(3):284-288
[113]孙书利,邓自立.带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器[J],控制理论与应用,2004,21(4): 635-638
[114]赵世锋,张涛,范耀祖.基于a trous算法的MEMS陀螺仪随机漂移建模[J].中国惯性技术学报,2007,2:96-99
[115] Ahmet Bulut, Ambuj K. Singh, SWAT:Hiearchical stream summarization in large networks[C], 19th international conference on data engineering,2003: 303-314
[116] Yinghui Kong,Jinsha Yuan etc. Online prediction of time series using incremental wavelet decomposition and surport vector machine[C].DRPT2008,6-9 April:2388-2402
[117]龚斌,金文,李兆南,金志浩.不同小波基在碳钢材料声发射信号分析中的应用[J],仪器仪表学报.2008,29(3):506-511
[118] D. Labarre, E.Grivel,M. Najim. Two-Kalman filters based instrumental variable techniques for speech enhancement[C].IEEE 6th workshop on multimedia signal processing, 2004:375-378
[119] K.Hasan, J.Hossain, A.Haque. Parameter estimation of multichannel auto-regressive processes in noise[J], Signal Process. 2003,83: 603–610
[120] Petitjean J.,Diversi R., Grivel E.,et. Recursive errors-in-variables approach for AR parameter estimation from noisy observations:Application to radar sea clutter rejection[C]. International Conference on Acoustics, Speech and Signal Processing-Proceedings, 2009:3401-3404
[121]黄操,孙振国,陈强,刘鹏飞.移动式修焊机器人双DSP嵌入式视觉反馈控制系统[J].清华大学学报(自然科学版),2009,49(2):198-201
[122]刘桂雄,邝泳聪,金军.基于测频测周方法集成的高分子湿度仪[J].华南理工大学学报, 2001,29(3):39-42
[123] Hu Chang-peng,Hong Xiao-bin,Ye Tingdong,Liu Gui-xiong. On-line test system of liquefied ethanol concentration based on soft-sensing technique[J]. Science Technology and Engineering, 2008,5:1183-1187
[124] Zongqing Lu, Xiong Zhang ,Chuiliang Sun. An Embedded System with uClinux based on FPGA[C]. IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application,2008:691-694
[125]刘桂雄,洪晓斌,刘劲光等.基于XML的IP智能测控系统跨平台思想的实现[J],制造业自动化,2006,28(4):4-7
[126]王端,傅丰.牵引变电所综合自动化系统中实时数据库技术[J].合肥工业大学学报,2009,32(9):1357-1361
[127]侯迪波.流程工业CIMS中GIS技术的应用[J],仪器仪表学报,2005,26(8):6-10
[128] B.P.J. de Lacy Costello,R.J. Ewen,N. Guernion et al. Highly sensitive mixed oxide sensors for the detection of ethanol[J], Sensors and Actuators B, 2002, 87:207-210
[129]逯家辉滕利荣蒋富明等.短波近红外光谱法分析酒中乙醇含量[J].吉林大学学报, 2003, 41(2): 87- 89
[130]付华,杜晓坤,许振良.超声波和电容传感器的两相流浓度测量装置及测量方法[ZL].发明专利:CN200510045723.4
[131]刘丽,张彤,漆奇,陈维友,徐宝琨.基于La0.7Sr0.3FeO3的微结构乙醇气体传感器的研制[J].半导体学报,2007,28(4):610-613
[132] P. Ivanov, E. Llobet, X. Vilanova et al. Development of high sensitivity ethanol gas sensors based on Pt-doped SnO2 surfaces[J]. Sensors and Actuators B.2004, 99(3):201-206
[133] Wan-young Chuang, Jun-woo Lim, Duk-dong Lee. Study on thermal properties of a micro gas sensing element array with central single heater[J].Sensors and Actuators B, 2002, 83: 281-284
[134] J.Riegel, H. Neumann,H.M.Wiedenmann. Exhaust gas sensors for automotive emission control[J], Solid state ionics, 2002:783-800
[135]甄永亮.基于气敏效应的微生物发酵乙醇浓度检测仪研究[D].北京化工大学硕士学位论文,2008
[136]叶廷东,刘桂雄,胡长鹏等.用于液态浓度在线监测的气液平衡建模机理[J].科学技术与工程, 2007.7:3889-3892
[137]华东理工大学.FC2002型乙醇检测流加控制器[OL].http://www.super xin -xi.com/sysms/yichun.doc,2002
[138]洪晓斌,刘桂雄,叶廷东等.基于INLR-PPLS的非线性多传感信息建模新方法[J],华南理工大学学报(自然科学版),2009,37(8):56-60
[2]中国仪器仪表协会.中国测量控制与仪器仪表中长期科技发展规划建设[R].2004,9
[3]中国仪器仪表协会.现代仪器仪表发展和未来五年我国对仪器仪表市场需求分析报告[R].2004,7
[4] Yanhua Ruan, Lang Hong. Use of the interacting multiple model algorithm with multiple sensors[J]. Mathematical and Computer Modelling, 2006,44(3):332-341
[5] Salahshoor Karim, Kordestani Mojtaba, Khoshro Majid S. Design of online soft sensors based on combined adaptive PCA and RBF neural networks[C]. IEEE Symposium on Computational Intelligence in Control and Automation,2009:89-95
[6] Yang Hai-lan, Cai Yan, Bao Ye-feng, Zhou Yun. Analysis and application of partial least square regression in arc welding process [J]. Central South University of Technology 2005,12(4):453-458
[7] R.Rego a,b,A.Mends. Carbon dioxide/methane gas sensor based on permselectivity of polymeric membranes for biogas monitoring[J]. Sensors and Actuators B.2004,103:2-6
[8] N.Rajabbeigi,B.Elyassi,A.Khodadadi,S.S.Mohajerzadeh,M.Sahimi.A novel minia -turized oxygen sensor with solid-state ceriazirconia reference[J]. Sensors and Actuators B.2004,100:139-142
[9] Gorban A.N., Kégl B.,Wunsch D.C et. Principal Manifolds for Data Visualization and Dimension Reduction [R]. Lecture Notes in Computational Science and Engineering. 2008,Vol.58:340
[10]付克昌.基于结构优化PCA的传感器故障诊断方法及其应用研究[D].浙江大学博士学位论文,2007
[11] Alexandre Perera, Niko Papamichail, Nicolae Barsan. et al. On-Line Novelty Detection by Recursive Dynamic Principal Component Analysis and Gas Sensor Arrays Under Drift Conditions[J]. Sensors Journal.2006,6(3):770-783
[12] N. Rivara, P.B. Dickinson, A.T. Shenton. A transient virtual-AFR sensor using the in-cylinder ion current signal[DB/OL]. http://www.sciencedirect.com/science. 2009,1
[13] T. Katsube, S. Umetani, Liqin Shi and Y. Hasegawa. Sensor Fusion for Taste Sensor and Odor Sensor[J]. Chemical Senses 2005,30(1): 260-264
[14] Geladi P,Kowalski B R.Partial least-squares regression:a tutorial[J]. Analytica Chimica Acta, 1986,185:1-17
[15] Hoskuldsson,A PLS regression methods[J].Chemometrics,1988,2: 211-228
[16] Lorber A,Wangen L E,Kowalski B R.A theoretical foundation for the PLS algorithm[J].Chemometrics,1987,1:19-31
[17] Yang Hai-lan, Cai Yan, Bao Ye-feng, Zhou Yun. Analysis and application of partial least square regression in arc welding process[J].Central South University of Technology 2005,12(4):453-458
[18] J.M.Karthikeya Udayagiri.V.R, Taleb Moazzeni et al. Detection algorithms for the Nano Nose[C]. 19th International Conference on Systems Engineering. 2008:399-404
[19]梁军.轧钢加热炉钢坯加热质量的检测研究(II)[J].传感技术学报. 2003,6(2):117 - 123
[20] D. Wang, R. Srinivasan, J. Liu et al. Data-driven Soft Sensor Approach For Quality Prediction in a Refinery Process[C]. IEEE International Conference on Industrial Informatics, 2006:230-235
[21]蔡艳,杨海澜,许轲,吴毅雄等.短路过渡弧焊过程稳定性在线评价模型设计[J].上海交通大学学报.2005,39(7):1038-1041
[22] Carlos Enrique Carleos Artime , Jes's Angel Baro de la Fuente et al. On-line Estimation of Fresh Milk Composition by means of VIS-NIR Spectrometry and Partial Least Squares Method[C].IEEE International Instrumentation and Measurement Technology Conference,Victoria, Vancouver Island, Canada, 2008,5:1-5
[23] Bin Zhang, Lei Deng, Qiao Gao et al. Fast Discrimination of Chocolate Varieties Using Near Infrared Spectroscopy[C]. Proceedings of the IEEE International Conference on Automation and Logistics.Qingdao, China , 2008,9:730-735
[24]徐惠荣,汪辉君,黄康等. PLS和SMLR建模方法在水蜜桃糖度无损检测中的比较研究[J].光谱学与光谱分析.2008,28(11):2523-2526
[25] Frank Dieterle,Stefan Busche,Günter Gauglitz. Different approaches to multivariate calibration of nonlinear sensor data[J]. Analytical and Bioanalytical Chemistry . 2004,380(3):383-396
[26]李春富.基于数据的软测量建模方法及其应用的研究[D].清华大学工学博士学位论文. 2004
[27] C. Li , H. Ye, G. Wang, J. Zhang, A Recursive Nonlinear PLS Algorithm for Adaptive Nonlinear Process Modeling[J]. Chemical Engineering & Technology. 2005,28(2):141-152
[28] Chunfu Li, Jie Zhang, Guizeng Wang. Batch-to-Batch Optimal Control of Batch Processes Based on Recursively Updated Nonlinear Partial Least Squares Models [J]. Chemical Engineering Communications. 2007, 194:261-279
[29] Haixian Wang, Zilan Hu .Maximum Margin Criterion Embedded Partial Least Square Regression for Linear and Nonlinear Discrimination[C]. Computational Intelligence and Security, 2006 vol.1:33-38
[30] Tenenhaus A., Guillemot V. , Gidrol X.. Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on PLS Regression [J],Computational Biology and Bioinformatics. 2008,8:1
[31] Wo1d S,Kettaneh- Wold N,Skagerberg B.Nonlinear PLS modeling[J],Chemometries and Intelligent Laboratory Systems.1989,7: 53-65
[32] Baffi, G.; Martin, E.B.; Morris, A.J. Non-linear projection to latent structures revisited:the quadratic PLS algorithm[J].Computers and Chemical Engineering. 1999,23(3): 395-411
[33] Wold S, Eriksson L, Sjo¨stro¨m M. PLS in chemistry. In Encyclopedia of Computational Chemistry, ed. Scleyer PVR[DB]. Wiley: New York, 1998; 2006–2016
[34]粱军,汪小勇,王文庆.基于神经网络PLS方法的软测量建模研究[J].浙江大学学报(工学版).2004,38(6),676-681
[35] Tang K,Li T,Combining PLS with GA-GP for QSAR [J],Chemometrics and Intelligent Laboratory Systems.2002,64:55-64
[36]成忠,陈德钊.模糊偏最小二乘及其在药物构效关系中的应用[J].浙江大学学报.2005,39(10):1613-1617
[37] Liqing Di,Zhihua Xiong, Xianhui Yang. Nolinear process modeling and optimization based on multiway kernel partial least squares model [C]. Proceedings of the Winter Simulation Conference, 2008:1645-1651
[38]张琳,张黎明,李燕等.多项式偏最小二乘法对非线性体系红外谱图的分析[J].光谱学与光谱分析.2006,26(4):620-623
[39] Baffi G,Martin EB,Morris AJ.Nonlinear projection to latent structure revisited:the quadr -atic partial least squares (PLS) algorithm [J].Compute Chem.Eng.1999,23 (4):395- 411
[40]于晓栋,黄德先,王雄.差分进化在基于非线性规划的多项式PLS中的应用[J].化工自动化及仪表, 2007, 34(4): 14-17
[41] Piwowar J.M,Ledrew E.F. ARMA time series modelling of remote sensing imagery: a new approach for climate change studies[J]. International Journal of Remote Sensing. 2002,23(24):5225-5248
[42]李捷,刘先省,韩志杰.基于ARMA的无线传感器网络流量预测模型的研究[J].电子与信息学报.2007,29(5):1224-1227
[43] Liu Datong; Peng Yu; Peng Xiyuan. Online Fault Prediction Based on Combined AOSVR and ARMA Models[C],Testing and Diagnosis.2009:1-4
[44] Zhu Wang,Henry L.Gray,Wayne A. Woodward. The Application of the Kalman Filter to Nonstationary Time Series through Time Deformation[J/OL]. https://smu.edu/statistics/ TechReports. 2008,3:1-33
[45]邓自立,郝钢.自校正分布式观测融合Kalman滤波器[J].电子与信息学报,2007,29(6): 1850-1854
[46] Benvensite A, Nikoukhah R,Willsky A. S. Multiscale system theory[J]. In:Proc. 29th IEEE conference on decision and control,Honolulu,HI,1990:2484-2487
[47] Chou K C,Willsky As,Benvensite A. Multiscale recursive estimation, data fusion, and regularization[J]. IEEE trans. On Automatic control,1994,39(3):464-478
[48] Chou K C,Willsky A s,Nikoukhah R. Multiscale system, Kalman filter, and riccati equations[J]. IEEE trans. On Automatic control,1994,39(3):479-492
[49] Basseville M, Benvensite A, Willsky A.S. Multiscale autoregressive process,Part I:Schur-Levinson parametrization[J].IEEE Trans. On signal processing,1992,40(8): 1915-1934
[50] K. Chou, S. A. Golden, A. S. Willsky. Multiresolution stochastic models, data fusion and wavelet transform[C]. Signal Processing, 1993, 34(3): 257-282
[51] L. Hong. Multiresolution distributed filtering[C]. IEEE traps. On Automatic Control, 1994, 39(4):853-856
[52]潘泉,张磊,崔培玲,张洪才.一类动态多尺度系统的最优滤波[J].中国科学(E辑),2004,34(4):433-447
[53]文成林,陈志国,闫莉萍,周东华.基于多速率传感器动态系统的多尺度递归融合估计[J];电子与信息学报.2003,25(3): 306-312
[54] Xiaoli Li, Xin Yao. Multi-scale statistical process monitoring in machining[J]. Industrial Electronics, IEEE Transactions on.2005,52(3):924-927
[55] Truchetet,Frédéric,Laligant,Olivier.Review of industrial applications of wavelet and multi-resolution based signal and image processing [J]. Electron.Imaging, 2008,17(9): 1412 -1430
[56] Rathinam, A,Padmini, S,Ravikumar, V. Application of supervised learning artificial neural networks [CPNN,BPNN] for solving power flow problem[C].ICTES 2007:156-160
[57] Lei Wang, Cheng Shao, Hai Wang, Hong Wu. Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases[J]. Journal of Natural Gas Chemistry, 2006,15(3) : 230-234
[58] Duduku Krishnaiah, D.M. Reddy Prasad, Awang Bono, et al. Application of ultrasonic waves coupled with functional link neural network for estimation of carrageenan concentration[J]. nternational Journal of Physical Sciences.2008, 3 (4):90-96
[59] Cortes C,Vapnic V.Support-vector networks[J].Machine Learning ,1995,20(3):273-297
[60] Abdul Md Mazid, A B M Shawkat Ali. Opto-tactile Sensor for Surface Texture Pattern Identification using Support Vector Machine[C],10th Intl. Conf. on Control, Automation, Robotics and Vision. Hanoi, Vietnam.2008,12:1830-1835
[61] Yonggang Li,Weihua Gui,Chunhua Yang,Zhisheng Chen. Distributed SVMs based soft sensor and its application for high pressure dissolving [C]. Intelligent Control and Automation, 2008: 5611-5615
[62] Vincent Bombardier,Cyril Mazaud,Pascal Lhoste et al. Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system[J]. Computers in Industry.2007, 58(4):355-366
[63]宋国民.多分力车轮力传感器研究及其在汽车道路试验中的应用[D].东南大学博士学位论文.2001
[64] Yinan Zhang,Qingwei Sun, He Quan et al. Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning[J].Journal of Systems Engineering and Electronics.2006,17(3):495-501
[64] S. Jassar, Z. Liao, L. Zhao. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems[DB/OL]. Building and Environment, available on sciencedirect.com,2008.10
[65] J.Holland. Genetic algorithms and classifier systems, Foundations and future directions,Genetic algorithms and their application[C], Proceedings of the second international conference on Genetic algorithms,1987:88-89
[66]芦俊,陈俊杰,颜景平.遗传算法在传感器非线性校正中的应用[J].传感器技术.2003,22(6):56-57,61
[67] Jamaluddin,Hishamuddin,Abd. Samad, M. F. et al. Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification[J]. Journal of Systems and Control Engineering, 2007,221 (7):975-989
[68] Kim, S, Kim, HS. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling[J].Journal of Hydrology,2008,351(3-4): 299-317
[69] Yang Zhangang; Che Yanbo; Cheng, K.W.E. Genetic Algorithm-Based RBF Neural Network Load Forecasting Model[C]. IEEE Power Engineering Society General Meeting, 2007.6:1- 6
[70] Celal Yildiz, Mustafa Turkmen New and very simple CAD models for coplanar waveguide synthesis [J], Microwave and Optical Technology Letters. 2004, 41(1):49-53
[71] Bo Li,Lagrange interpolation and finite element superconvergence[J],Numerical Methods for Partial Differential Equations,2004,24(1):33-59
[72] Krassimira Vlachkova,A Newton-type algorithm for solving an extremal constrained interpolation problem[J].Numerical Linear Algebra with Applications,2000,7(3):133-146
[73] Thomas Kunkle, Multivariate Differences, Polynomials, and Splines[J], Journal of Approximation theory 1996,84:290_314
[74]余跃,冯志刚.分形插值样条的定义以及计算研究[J].工程图学学报,2008,4:86-90
[75]吕春兰,梁伟,杨世儒.高准确度的传感器建模方法及应用[J].传感器技术.2003,22 (5) :3-5
[76]李建利,房建成,盛蔚.MEMS陀螺标度因数误差分析及分段插值补偿[J].北京航空航天大学学报.2007,9:1064-1067
[77] Meng-Fu Wang,F. T. K. Au. Precise integration methods based on Lagrange piecewise interpolation polynomials[DB/OL]. International Journal for Numerical Methods in Engineering.Published Online 2008
[78] Stevan Dubljevic, Panagiotis D. Christofides, Ioannis G. Kevrekidis. Distributed nonlinearcontrol of diffusion-reaction processes[J]. International Journal of Robust and Nonlinear Control. 2004, 14(2):133-156
[79] Jin H.Z., Lu H.,Cho S.K et al. Nonlinear compensation for non-contact electronic joystick with a single hall senso[J].Science, Measurement & Technology, IET.2008,2(1):9-17
[80] D. Labarre, E.Grivel, Y.Berthoumieu, et al. Consistent estimation of autoregressive parameters from noisy observations based on two interacting Kalman filters[J]. Signal processing, 2006,86:2863-2876
[81]文成林.多尺度动态建模理论及其应用[M].北京:科学出版社. 2008:1-20
[82] Shi Jian,Liu Xinggao. Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis[J]. Chinese Journal of Chemical Eingerring. 2005, 13 (6):849-852
[83]林继鹏,刘君华.基于小波的支持向量机算法研究[J].西安交通大学学报.2005,39(8):816-819
[84] Harri N,Teri H,Ari K et al.Evolving the neural network model for forecasting air pollution time series[J]. Engineering Applications of Artificial Intelligence, 2004,17:159-167
[85]谷吉海,姜兴渭,王晓锋等.双向Elman神经网络在卫星电池阵功率预测中的应用研究[J].南京理工大学学报,2004,28(5):405-409
[86]刘清.考虑测量噪声的传感器动态测量误差补偿[J].江苏大学学报,2007,27(2):160-163
[87] FU HuiMin,Progressive autogressive prediction method[J].Joumal of Mechanical Strength,2006, 28(1):34-39
[88]黄永梅,张桐,唐涛等.卡尔曼预测滤波对跟踪传感器延迟补偿的算法研究[J].光电工程,2006,33(6):4-9
[89]王风宇,云晓春,申伟东.基于小波变换的网络流量在线预测模型[J].高技术通讯,2006,16(12):1219-1225
[90] Olivier Renaud, Jean-Luc Starck, Fionn Murtagh. Wavelet-Based Combined Signal Filtering and Prediction[J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS -PART B: CYBERNETICS, 2005,35(6):1241-1251
[91]孙振明,姜兴渭,王晓锋等.a Trous小波在卫星遥测数据递归预测中的应用[J].南京理工大学学报,2004,28(6):606-611
[92] Xu Kejun, Li Cheng. Dynamic Decoupling and compensation methods of multi-axis force sensors[J].IEEE Transactions on Instrumentation and Measure -ment.2000 , 49(5): 935-941
[93] Dongchuan Yu, Qinghao Meng, Jiang Wang, et al. Neural network based left-inverse system dynamic decoupling & compensating method of multi-dimension sensors[J]. American Control Conference, 2005, 6(3): 1727-1732
[94]刘桂雄,李夏妮,周德光,基于多尺度数值计算的传感信息解耦新方法[J].光学精密工程, 2005,13: 164-167
[95]丁明理,梁宏,王祁等.基于小生境遗传算法的多维传感器动态解耦方法[J].传感器技术学报,2006,19(3):667-671
[96] Silvia L.T. Lima et al.PLS Pruning : a new approach to variable seleetion for multivariate calibration based on Hessian matrix of errors[J]. Chemometrics and intelligent Laboratory System.2005.76(1):6-11
[97] Frank Dieterle,Stefan Busche,Günter Gauglitz. Different approaches to multivariate calibration of nonlinear sensor data. Analytical and Bioanalytical Chemistry . 2004, 380(3):383-396
[98]李军会;秦西云;张文娟等.局部偏最小二乘回归建模参数对近红外检测结果的影响研究[J].光谱学与光谱分析.2007,27(2):262-264
[99] Fei Liu,Yong He, Li Wang . Discrimination of Varieties of Yellow Wines by Using Vis /NIR Spectroscopy and PLS-BP Model [C]. Control and Automation, 2007,5: 1492-1495
[100] Jun Chi-Hyuck, Sang-Ho Lee, Hae-Sang Park, Jeong-Hwa Lee. Use of partial least squares regression for variable selection and quality prediction [C]. Computers & Industrial Engineering, 2009:1302-1307
[101]丁光辉. PLS和GA应用于部分有机污染物的QSAR研究[M],大连理工大学博士学位论文.2006
[102]刘桂雄,林绪洪.鱼类超微弱发光的偏最小二乘回归分析与建模[J].华南理工大学学报(自然科学版), 2006, 34(11): 29-32
[103] Yumin He,Xuefeng Chen, Jiawei Xiang,et al. Multiresolut-ion analysis for finite element method using interpolating wavelet and lifting scheme [DB/OL]. Communications in numeric methods in engineering, publised online in Wiley InterScience 2007
[104] Olivier Renaud,Jean-Luc Starck,Fionn Murtagh. Prediction based on a multiscale decomposition[J]. Int.J.Wavelets, Multires. And Inform process, 2003,1(2):217-232
[105]张弦,王宏力.基于最优分解尺度的静态提升小波去噪方法[J].中国电机工程学报,2009,35(3):501-508
[106] Kaew pijit S, Le moigne J, El-Ghazawi T. Automatic reduction of hyperspectral imagery using wavelet spectral analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 863-871
[107]刘桂雄,李夏妮,周德光,基于多维插值补偿其它因素对传感特性影响的数学方法[J].光学精密工程,2004,12(3):258-261
[108]叶廷东,刘桂雄,黄国健,陈铁群.基于多尺度逼近的多维传感信息解耦新方法[J].华南理工大学学报,2009,4:45-49
[109] ERIC Miller, Alan S. Willsky, A Multiscale approach to sensor fusion and the solution of linear inverse problems[J]. Applied and Computational Harmonic Analysis 1995,2:127-147
[110]仲崇权,董西路,张立勇,曹阳.多传感器测量中方差估计[J].数据采集与处理,2003,18(4):412-417
[111] Roal J R, Girja G. Sensor data fusion algorithms using square-root information filtering[J]. IEEE Proc Radar Sonar Naving,2002,149(2):89-96
[112]姜力,刘宏,蔡鹤皋.多维力/力矩传感器静态解耦的研究[J].仪器仪表学报,2004,25(3):284-288
[113]孙书利,邓自立.带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器[J],控制理论与应用,2004,21(4): 635-638
[114]赵世锋,张涛,范耀祖.基于a trous算法的MEMS陀螺仪随机漂移建模[J].中国惯性技术学报,2007,2:96-99
[115] Ahmet Bulut, Ambuj K. Singh, SWAT:Hiearchical stream summarization in large networks[C], 19th international conference on data engineering,2003: 303-314
[116] Yinghui Kong,Jinsha Yuan etc. Online prediction of time series using incremental wavelet decomposition and surport vector machine[C].DRPT2008,6-9 April:2388-2402
[117]龚斌,金文,李兆南,金志浩.不同小波基在碳钢材料声发射信号分析中的应用[J],仪器仪表学报.2008,29(3):506-511
[118] D. Labarre, E.Grivel,M. Najim. Two-Kalman filters based instrumental variable techniques for speech enhancement[C].IEEE 6th workshop on multimedia signal processing, 2004:375-378
[119] K.Hasan, J.Hossain, A.Haque. Parameter estimation of multichannel auto-regressive processes in noise[J], Signal Process. 2003,83: 603–610
[120] Petitjean J.,Diversi R., Grivel E.,et. Recursive errors-in-variables approach for AR parameter estimation from noisy observations:Application to radar sea clutter rejection[C]. International Conference on Acoustics, Speech and Signal Processing-Proceedings, 2009:3401-3404
[121]黄操,孙振国,陈强,刘鹏飞.移动式修焊机器人双DSP嵌入式视觉反馈控制系统[J].清华大学学报(自然科学版),2009,49(2):198-201
[122]刘桂雄,邝泳聪,金军.基于测频测周方法集成的高分子湿度仪[J].华南理工大学学报, 2001,29(3):39-42
[123] Hu Chang-peng,Hong Xiao-bin,Ye Tingdong,Liu Gui-xiong. On-line test system of liquefied ethanol concentration based on soft-sensing technique[J]. Science Technology and Engineering, 2008,5:1183-1187
[124] Zongqing Lu, Xiong Zhang ,Chuiliang Sun. An Embedded System with uClinux based on FPGA[C]. IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application,2008:691-694
[125]刘桂雄,洪晓斌,刘劲光等.基于XML的IP智能测控系统跨平台思想的实现[J],制造业自动化,2006,28(4):4-7
[126]王端,傅丰.牵引变电所综合自动化系统中实时数据库技术[J].合肥工业大学学报,2009,32(9):1357-1361
[127]侯迪波.流程工业CIMS中GIS技术的应用[J],仪器仪表学报,2005,26(8):6-10
[128] B.P.J. de Lacy Costello,R.J. Ewen,N. Guernion et al. Highly sensitive mixed oxide sensors for the detection of ethanol[J], Sensors and Actuators B, 2002, 87:207-210
[129]逯家辉滕利荣蒋富明等.短波近红外光谱法分析酒中乙醇含量[J].吉林大学学报, 2003, 41(2): 87- 89
[130]付华,杜晓坤,许振良.超声波和电容传感器的两相流浓度测量装置及测量方法[ZL].发明专利:CN200510045723.4
[131]刘丽,张彤,漆奇,陈维友,徐宝琨.基于La0.7Sr0.3FeO3的微结构乙醇气体传感器的研制[J].半导体学报,2007,28(4):610-613
[132] P. Ivanov, E. Llobet, X. Vilanova et al. Development of high sensitivity ethanol gas sensors based on Pt-doped SnO2 surfaces[J]. Sensors and Actuators B.2004, 99(3):201-206
[133] Wan-young Chuang, Jun-woo Lim, Duk-dong Lee. Study on thermal properties of a micro gas sensing element array with central single heater[J].Sensors and Actuators B, 2002, 83: 281-284
[134] J.Riegel, H. Neumann,H.M.Wiedenmann. Exhaust gas sensors for automotive emission control[J], Solid state ionics, 2002:783-800
[135]甄永亮.基于气敏效应的微生物发酵乙醇浓度检测仪研究[D].北京化工大学硕士学位论文,2008
[136]叶廷东,刘桂雄,胡长鹏等.用于液态浓度在线监测的气液平衡建模机理[J].科学技术与工程, 2007.7:3889-3892
[137]华东理工大学.FC2002型乙醇检测流加控制器[OL].http://www.super xin -xi.com/sysms/yichun.doc,2002
[138]洪晓斌,刘桂雄,叶廷东等.基于INLR-PPLS的非线性多传感信息建模新方法[J],华南理工大学学报(自然科学版),2009,37(8):56-60