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第24届中国控制与决策会议

作者： Fei Chu, Fuli Wang, Xiaogang Wang, Shuning Zhang College of Information Science and Engineering, Northeastern University, No.11, Lane 3, WenHua Road, HePing District, Shenyang-110819, Liaoning, China

The change of the performance of gas turbine power plant may be dramatic under off-design conditions. To describe the off-design performance of gas turbine power plant, good prediction tools are essential. The objective of this paper is to asses the feasibility of the kernel partial least squares (KPLS) technique in performance prediction of gas turbine power plant under off-design conditions. Historical data from the real industrial gas-steam combined cycle of a cogeneration plant unit were used to train KPLS regression models and the KPLS parameters, such as the number of latent variables, were determined by a 5-fold cross-validation with the root-mean-squared-error. Results obtained by KPLS models are compared with the measured data. It was shown that, under given off-design conditions, the KPLS tool was able to predict the unit load and gas flow with a high degree of accuracy.

关键词： Gas Turbine Power Plant kernel partial least squares Performance Prediction Off-design Conditions Load Gas Flow

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Relief wrapper based kernel partial least squares subspace selection

Relief wrapper based Kernel Partial Least Squares subspace s...

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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

作者： Buqun Zhang Shangzhi Zheng Department of Computer Science and Technology Chaohu University Chaohu P.R.C Hualong Bu* Jing Xia Department of Computer Science and Technology Chaohu University Chaohu P.R.C

kernel partial least squares method can obtain nonlinear novel features for further classification and other tasks,the dimension of extracted kernel space is usually very high,there still may contain irrelevant and redundant features,so using feature selection to select the most discriminative and informative features for classification or data analysis is important,but there are few attentions to it until *** we propose a novel method which firstly uses kernel partial least squares as a nonlinear feature extraction method to get a basis set,and then uses the Relief Wrapper,one of the hybrid feature selection algorithms,to select the most discriminative *** selected features form a subspace of the kernel space,where different state-of-the-art classification algorithms can be applied for *** results on three microarray datasets validate the efficiency and accuracy of our method.

关键词： relief wrapper kernel partial least squares Feature Extraction kernel Subspace Selection

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QSAR Studies on PCDD/Fs by kernel PLS

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Chemical Research in Chinese Universities 2008年第5期24卷 541-545页

作者： TANG Kai-lin LI Tong-hua CHEN Kai Department of Chemistry Tongji University Shanghai 200092 P. R. China

QSPR models of PCDD/Fs were generated by means of kernel partial least squares. The molecular distance-edge vector method was used as descriptors to get model I for predicting PCDD/Fs retention behavior. The chlorinated positions were also used and model II was obtained. In studied cases, the predictive ability of the KPLS model is comparable or superior to those of PLS and ANN. The results indicate that KPLS can be used as an alternative powerful modeling tool for QSPR studies.

关键词： QSPR modeling kernel partial least squares PCDD/Fs

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维普期刊数据库

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Improved kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

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Chinese Journal of Chemical Engineering 2014年第6期22卷 657-663页

作者：王丽侍洪波 School of Electrical and Electronic Engineering Shanghai Institute of Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

关键词： nonlinear process fault detection kernel partial least squares statistical local approach

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维普期刊数据库

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Extending AOTF-NIR Spectrometer to Gas Measurement

Extending AOTF-NIR Spectrometer to Gas Measurement

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2007 8th International Conference on Electronic Measurement & Instruments（ICEMI’2007）

作者： Hao Huimin~(1,2) Pan Mi~3 Liu Junhua~1 (1 Dep.of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China) (2 Automatic Company,Taiyuan Iron and Steel Co.,Taiyuan 030003,China) (3 Sanxi Yinhe electronics Co.Ltd,Taiyuan,030006,China)

As a new generation spectrometers,Acousto-Optic Tunable Filter(AOTF)-Near Infrared(NIR) spectrometer had been widely applied in many domains due to its remarkable advantages such as higher resolution,faster and more robust. But,to date,the availability of AOTF-NIR spectrometer is limited to liquid and solid *** this paper,the application of AOTF-NIR spectrometer in gas quantitative determination is explored.A vacuumable gas cell was designed and assembled to Luminar 5030-731 AOTF-NIR spectrometer for obtaining spectra of gas samples including methane(CH) and carbon monoxide(CO),and its *** detected gas samples of different concentrations are prepared dynamically by diluting standard gas with super pure nitrogen via precision volumetric gas flow *** test the performance of refitted spectrometer,gas samples were injected into the gas cell and the spectra of wavelength between 1100nm-2300nm were *** relative absorbency of CH4 of different concentration was *** shows obvious proportional increase along with the concentration *** indicates that the AOTF-NIR spectrometer mounted gas cell can be used for scanning gas samples and obtain exact *** then, prediction model of unknown concentration gas mixtures of CH4 and CO was created by using kernel partial least squares (KPLS).The effectiveness of the model is tested by the samples of predict *** Mean Square Error(MSE) of the predicted gas is below 1.64%when the concentrations of component gas are over 0.40%.The exploratory work indicates a new way for gas detection and provides the base for developing of NIR gas detector.

关键词： Quantitative Analysis Acousto-Optic Tunable Filter Near Infrared kernel partial least squares

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