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relevance vector machine technique for the inverse scattering problem

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Chinese Physics B 2012年第5期21卷 19-24页

作者：王芳芳张业荣 School of Electronic Science and Engineering Nanjing University of Posts and Telecommunications

A novel method based on the relevance vector machine（RVM） for the inverse scattering problem is presented in this paper.The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered.The nonlinearity is embodied in the relation between the scattered field and the target property,which can be obtained through the RVM training process.Besides,rather than utilizing regularization,the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output.Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy,convergence,robustness,generalization,and improved performance in terms of sparse property in comparison with the support vector machine（SVM） based approach.

关键词： inverse scattering problem through-wall problem relevance vector machine finite-difference time-domain

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

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Chinese Journal of Chemical Engineering 2021年第6期34卷 106-115页

作者： Zhiquan Wang Liang Wang Zhihong Yuan Bingzhen Chen Department of Chemical Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

关键词： Methanol to olefins relevance vector machine Genetic algorithm Operation optimization Systems engineering Process systems

Seismic liquefaction potential assessment by using relevance vector machine

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Earthquake Engineering and Engineering Vibration 2007年第4期6卷 331-336页

作者： Pijush Samui Department of Civil Engineering Indian Institute of Science Bangalore 560012 India

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the relevance vector machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

关键词： liquefaction cone penetration test relevance vector machine artificial neural network

Prediction of Compressive Strength of Various SCC Mixes Using relevance vector machine

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Computers, Materials & Continua 2018年第1期54卷 83-102页

作者： G.Jayaprakash M.P.Muthuraj Assistant Professor Dept.Civil Engg.KGISL Institute of TechnologyCoimbatore-641035India Assistant Professor Dept.Civil Engg.Coimbatore Institute of TechnologyCoimbatore-641014 India

This paper discusses the applicability of relevance vector machine(RVM)based regression to predict the compressive strength of various self compacting concrete(SCC)mixes.Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio,water binder ratio and steel fibres.relevance vector machine(RVM)is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification.The RVM has an identical functional form to the support vector machine,but provides probabilistic classification and regression.RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation.Compressive strength model has been developed by using MATLAB software for training and prediction.About 75%of the data has been used for development of model and 30%of the data is used for validation.The predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature.

关键词： relevance vector machine Self-compacting concrete Compressive strength Variance

Study on Classification Model Based on relevance vector machine with Genetic Algorithm

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Study on Classification Model Based on Relevance Vector Mach...

On Guidance Parameter Calculation of Flight Vehicle Using relevance vector machine and Particle Swarm Optimization

2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Yanhong Li Mathematical College Beihua University Jilin 132013 China Taihui Liu Computer College Beihua University Jilin 132013 China

A novel classification method based on relevance vector machine with genetic algorithm is presented in the paper.In the model,genetic algorithm is applied to gain the suitable training parameters of relevance vector machine.State classification of roll bearing is applied to testify the classification ability of the proposed method,and state classification data of roll bearing are given.The experimental results show that relevance vector machine with genetic algorithm has higher classification accuracy than backpropagation neural network and support vector machine.

关键词： relevance vector machine classification model genetic algorithm Bayesian

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On Guidance Parameter Calculation of Flight Vehicle Using Re...

An Integrated Method for Lithium-ion Batteries Remaining Useful Life Prediction Based on Unscented Particle Filter and relevance vector machine

第三十九届中国控制会议

作者： Yao Zhao Yun Tan Hai-tao Tian Ying Zou Beijing Aerospace Automatic Control Institute

To deal with the problem that guidance parameter calculation of flight vehicle requires a lot of computer memory space to store interpolation data tables, a novel guidance parameter calculation method based on relevance vector machine（RVM） and particle swarm optimization（PSO） is presented. By utilizing non-linear mapping property of RVM, parameter regression model for calculating guidance parameter is constructed. The model is trained based on the samples which are generated from the interpolation data tables of flight control trajectories. During training, the Gaussian kernel parameter of RVM is automatically selected by the use of PSO. Simulations on non-linear function approximation and flight vehicle data show that PSO-RVM model achieves high regression accuracy and the structure of model is much sparser. The guidance parameter can be calculated by storing less parameter in the controller of flight vehicle, and the presented method has some engineering application value.

关键词： Guidance parameter calculation flight vehicle relevance vector machine particle swarm optimization

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An Integrated Method for Lithium-ion Batteries Remaining Use...

Probabilistic analysis of tunnel displacements based on correlative recognition of rock mass parameters

第40届中国控制会议

作者： Meng Jiang Zhenxing Liu Yong Zhang Jie He Yujie Chen School of Information Science and Engineering Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education

Accurate remaining useful life（RUL） prediction for lithium-ion batteries plays a crucial role to prevent accidents from happening. In this paper, we proposed an hybrid method based on unscented particle filter（UPF） and relevance vector machine（RVM）, where the genetic algorithm optimized support vector regression（GA-SVR） was uesd to correct the error generated in the prediction process. RVM is first used to extract feature points and initialize degradation model. In order to implement the RUL prediction, UPF is employed to extrapolate trends of the battery degradation capacity, where the unscented Kalman filter（UKF）is applied to generate a proposal distribution as an importance function for PF. In addition, GA-SVR is uesd to correct prediction error. Finally, to verify the effect of the proposed UPF-GASVR method with higher prediction accuracy, NASA battery dataset was used to carry out the test and compare with some classical methods.

关键词： Remaining useful life prediction Unscented particle filter relevance vector machine Genetic algorithm Support vector regression

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Geoscience Frontiers 2021年第4期12卷 46-60页

作者： Minzong Zheng Shaojun Li Hongbo Zhao Xiang Huang Shili Qiu State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanHubei 430071China University of Chinese Academy of Sciences Beijing 100049China School of Civil and Architectural Engineering Shandong University of TechnologyZiboShandong 255000China

Displacement is vital in the evaluations of tunnel excavation processes,as well as in determining the postexcavation stability of surrounding rock masses.The prediction of tunnel displacement is a complex problem because of the uncertainties of rock mass properties.Meanwhile,the variation and the correlation relationship of geotechnical material properties have been gradually recognized by researchers in recent years.In this paper,a novel probabilistic method is proposed to estimate the uncertainties of rock mass properties and tunnel displacement,which integrated multivariate distribution function and a relevance vector machine(RVM).The multivariate distribution function is used to establish the probability model of related random variables.RVM is coupled with the numerical simulation methods to construct the nonlinear relationship between tunnel displacements and rock mass parameters,which avoided a large number of numerical simulations.Also,the residual rock mass parameters are taken into account to reflect the brittleness of deeply buried rock mass.Then,based on the proposed method,the uncertainty of displacement in a deep tunnel of CJPL-II laboratory are analyzed and compared with the in-situ measurements.It is found that the predicted tunnel displacements by the RVM model closely match with the measured ones.The correlations of parameters have significant impacts on the uncertainty results.The uncertainty of tunnel displacement decreases while the reliability of the tunnel increases with the increases of the negative correlations among rock mass parameters.When compared to the deterministic method,the proposed approach is more rational and scientific,and also conformed to rock engineering practices.

关键词： Uncertainties Correlation Displacement Multivariate distributions relevance vector machine

Application of RVM for prediction of bead shape in underwater rotating arc welding

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China Welding 2010年第4期19卷 40-43页

作者：杜健辉石永华王国荣黄国兴 School of Mechaniecal ＆ Automotive Engineering South ChinaUniversity of Technology Guangzhou 510640

Bead sttape in underwater rotating arc welding was affected by several welding parameters. RVM （ relevance vector machine） was used to build a model to predict weld bead shape. The training data set of RVM eortsists of the welding parameters which are rotational frequency, rotational radius, height of torch and welding current and the features of the bead shape. The maximum error and mean error for prediction of width are 0. 10 mm and 0. 09 mm, respectively, and the maximum error and mean error for prediction of penetration are 0. 31 mm and 0. 12mm, respectively, which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.

关键词： underwater welding relevance vector machine prediction model rotating arc sensor