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Petroleum Science 2021年第4期18卷 1230-1239页

作者： Venkata Vijayan S Hare Krishna Mohanta Ajaya Kumar Pani Department of Chemical Engineering Birla Institute of Technology and SciencePilaniRajasthan333031India

Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process *** article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation *** this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)*** different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also *** show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time.

关键词： Adaptive soft sensor Just in time learning regression support vector regression Naphtha boiling point

Deformation resistance prediction of tandem cold rolling based on grey wolf optimization and support vector regression

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Journal of Iron and Steel Research International 2023年第9期30卷 1803-1820页

作者： Ze-dong Wu Xiao-chen Wang Quan Yang Dong Xu Jian-wei Zhao Jing-dong Li Shu-zong Yan National Engineering Technology Research Center of Flat Rolling Equipment University of Science and Technology BeijingBeijing 100083China

In the traditional rolling force model of tandem cold rolling mills,the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material,which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip *** inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process,and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression(SVR)*** grey wolf optimization(GWO)algorithm was used to optimize the hyperparameters in the SVR model,and a deformation resistance prediction model based on GWO–SVR was *** with the traditional model,the GWO–SVR model shows different degrees of improvement in each stand,with significant improvement in stands S3–*** prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling *** head rolling force had a similar degree of improvement in accuracy to the deformation resistance,and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously ***,the thickness quality and shape quality of the strip head were improved accordingly,and the application results were consistent with expectations.

关键词： Tandem cold rolling Cross-process data application Deformation resistance prediction support vector regression Grey wolf optimization Rolling force accuracy

A Metamodeling Method Based on support vector regression for Robust Optimization

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Chinese Journal of Mechanical Engineering 2010年第2期23卷 242-251页

作者： XIANG Guoqi HUANG Dagui School of Mechatronics Engineering University of Electronic Science and Technology of China Chengdu 610054 China School of Mechanical Engineering Panzhihua University Panzhihua 617000 China

Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.

关键词： support vector regression metamodeling robust optimization genetic algorithm

Combination Computing of support vector Machine, support vector regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

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Chinese Journal of Chemical Physics 2016年第5期29卷 629-634,I0002页

作者：陈茜乔连生蔡漪涟张燕玲李贡宇北京中医药大学中药学院中药基础与新药研究重点实验室北京100102

The computational approaches of support vector machine （SVM）, support vector regression （SVR） and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 （CYP1A2） inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biolog-ical activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identi ed to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding a n-ity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug inter-actions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.

关键词： support vector machine support vector regression Molecular docking CYPIA2 inhibitor

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

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Building Simulation 2022年第3期15卷 317-331页

作者： Rui Tang Cheng Fan Fanzhe Zeng Wei Feng Institute for Environmental Design and Engineering University College LondonLondonUK Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina School of Municipal and Environmental Engineering Shenyang Jianzhu UniversityShenyangChina China Energy Group Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power *** this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor *** study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR *** to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control ***,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand *** results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature *** with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

关键词： demand response support vector regression machine learning building peak demand model predictive control smart grid

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

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Journal of Central South University 2015年第2期22卷 511-521页

作者： Saeid Shokri Mohammad Taghi Sadeghi Mahdi Ahmadi Marvast Shankar Narasimhan Department of Chemical Engineering Iran University of Science and Technology (IUST) Tehran 16765?163 Iran Process & Equipment Technology Development Division Research Institute of Petroleum Industry (RIPI) Tehran 14665?137 Iran Department of Chemical Engineering IIT Madras Chennai 600036 India

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

关键词： soft sensor support vector regression principal component analysis wavelet transform hydrodesulfurization process

Particle Swarm Optimization Based support vector regression for Blind Image Restoration

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Journal of Computer Science & Technology 2012年第5期27卷 989-995页

作者： Ratnakar Dash Pankaj Kumar Sa Banshidhar Majhi Computer Science and Engineering Department National Institute of Technology RourkelaRourkela 769008India

This paper presents a swarm intelligence based parameter optimization of the support vector machine （SVM） for blind image restoration. In this work, SVM is used to solve a regression problem. support vector regression （SVR） has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization （PSO） technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.

关键词： image restoration support vector regression particle swarm optimization

Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using support vector regression

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Journal of Systems Science & Complexity 2017年第6期30卷 1425-1442页

作者： HU Zhongyi BAO Yukun CHIONG Raymond XIONG Tao School of Information Management Wuhan University School of Management Huazhong University of Science and Technology School of Electrical Engineering and Computing The University of Newcastle College of Economics and Management Huazhong Agricultural University

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.

关键词： Financial market investment trading strategy parameter optimization stock index forecasting support vector regression

Cloud removal of remote sensing image based on multi-output support vector regression

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Journal of Systems Engineering and Electronics 2014年第6期25卷 1082-1088页

作者： Gensheng Hu Xiaoqi Sun Dong Liang Yingying Sun Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Electronics and Information Engineering Anhui University

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

关键词： remote sensing image cloud removal support vector regression multi-output

Predicting Chinese Abbreviations from Definitions:An Empirical Learning Approach Using support vector regression

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Journal of Computer Science & Technology 2008年第4期23卷 602-611页

作者：孙栩王厚峰王波 Institute of Computational Linguistics School of Electronics Engineering and Computer Science

In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however make keyword-based approaches less effective. This paper presents an empirical learning approach to Chinese abbreviation prediction. In this study, each abbreviation is taken as a reduced form of the corresponding definition （expanded form）, and the abbreviation prediction is formalized as a scoring and ranking problem among abbreviation candidates, which are automatically generated from the corresponding definition. By employing support vector regression （SVR） for scoring, we can obtain multiple abbreviation candidates together with their SVR values, which are used for candidate ranking. Experimental results show that the SVR method performs better than the popular heuristic rule of abbreviation prediction. In addition, in abbreviation prediction, the SVR method outperforms the hidden Markov model （HMM）.

关键词： statistical natural language processing abbreviation prediction support vector regression word clustering