检索结果-南通市图书馆

System Identification Modeling of Ship Manoeuvring Motion in 4 Degrees of Freedom Based on support vector machines

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Ocean Engineering 2015年第4期29卷 519-534页

作者：王雪刚邹早建余龙蔡韡 School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Jiujiang Branch of No.707 Research Institute China Shipbuilding Industry Corporation

Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20°/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.

关键词： ship manoeuvring 4 degrees of freedom system identification support vector machines

Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2013年第3期26卷 554-562页

作者： Ma Yingkun Xie Shilin Zhang Xinong Luo Yajun State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Xi'an Jiaotong University

A hybrid calibration approach based on support vector machines （SVM） is proposed to characterize nonlinear cross coupling of multi-dimensional transducer. It is difficult to identify these unknown nonlinearities and crosstalk just with a single conventional calibration approach. In this paper, a hybrid model comprising calibration matrix and SVM model for calibrating linearity and nonlinearity respectively is built up. The calibration matrix is determined by linear artificial neural network （ANN）, and the SVM is used to compensate for the nonlinear cross coupling among each dimension. A simulation of the calibration of a multi-dimensional sensor is conducted by the SVM hybrid calibration method, which is then utilized to calibrate a six-component force/torque transducer of wind tunnel balance. From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix. Moreover, with the calibration matrix, the hybrid model can provide a basis for the design of transducers.

关键词： Hybrid Multi-dimensional Nonlinear coupling support vector machines Transducers

Image denoising using least squares wavelet support vector machines

在线全文

学校读者我要写书评

暂无评论

Chinese Optics Letters 2007年第11期5卷 632-635页

作者：曾国平赵瑞珍 Institute of Information Science Beijing diaotong University Beijing 100044

We propose a new method for image denoising combining wavelet transform and support vector machines （SVMs）. A new image filter operator based on the least squares wavelet support vector machines （LSWSVMs） is presented. Noisy image can be denoised through this filter operator and wavelet thresholding technique. Experimental results show that the proposed method is better than the existing SVM regression with the Gaussian radial basis function （RBF） and polynomial RBF. Meanwhile, it can achieve better performance than other traditional methods such as the averaee filter and median filter.

关键词： Mathematical operators Polynomials support vector machines Wavelet transforms

Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers

在线全文

学校读者我要写书评

暂无评论

Transportation Safety and Environment 2020年第2期2卷 120-132页

作者： Stephen A.Arhin Adam Gatiba Howard University Transportation Research Center 23006th Street NWWashingtonDC 20059United States

The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic cost of these fatalities has been estimated to be in the millions of dollars.It is therefore necessary to investigate the predictability of the occurrence of theses crashes,based on pertinent factors,in order to provide mitigating measures.This research focused on the development of models to predict the injury severity of crashes using support vector machines(SVMs)and Gaussian naïve Bayes classifiers(GNBCs).The models were developed based on 3307 crashes that occurred from 2008 to 2015.Eight SVM models and a GNBC model were developed.The most accurate model was the SVM with a radial basis kernel function.This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%.The GNBC produced the worst-performing model with an accuracy of 48.5%.These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.

关键词： crashes unsignalized intersection support vector machines Gaussian naïve Bayes classifier injury severity

Study of tide prediction method influenced by nonperiodic factors based on support vector machines

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Oceanologica Sinica 2012年第5期31卷 160-164页

作者： HE Shi-jun ZHOU Wenjun ZHOU Ruyan HUANG Dongmei College of information Shanghai Ocean UniversityShanghai 201306China

Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine （SVM） to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability.

关键词： tidal prediction support vector machines celestial motion law harmonic analysis BP neural network nonperiodic factors

Drifting model approach to modeling based on weighted support vector machines

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2004年第4期15卷 610-614页

作者： FengRui SongChunlin ShaoHuihe DepartmentofAutomation ShanghaiJiaotongUniversityShanghai200030P.R.China DepartmentofComputerScience NortheasternUniversityShenyang110003P.R.China

This paper proposes a novel drifting modeling (DM) method. Briefly, we first employ an improved SVMs algorithm named weighted support vector machines (W.SVMs), which is suitable for locally learning, and then the DM method using the algorithm is proposed. By applying the proposed modeling method to Fluidized Catalytic Cracking Unit (FCCU), the simulation results show that the property of this proposed approach is superior to global modeling method based on standard SVMs.

关键词： vectors Data models Computational modeling support vector machines Estimation Petroleum

A Fast Algorithm for Training Large Scale support vector machines

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2022年第12期10卷 1-15页

作者： Mayowa Kassim Aregbesola Igor Griva George Mason University Fairfax USA

The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.

关键词： SVM Machine Learning support vector machines FISTA Fast Projected Gradient Augmented Lagrangian Working Set Selection Decomposition

A novel excitation controller using support vector machines and approximate models

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

控制理论与应用（英文版） 2008年第3期6卷 239-245页

作者： Xiaofang YUAN Yaonan WANG Shutao LI College of Electrical and Information Engineering Hunan University Changsha Hunan 410082 China

This paper proposes a novel excitation controller using support vector machines （SVM） and approximate models. The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion, which not only avoids complex control development and intensive computation, but also avoids online learning or adjustment. Only a general SVM modelling technique is involved in both model identification and controller implementation. The robustness of the stability is rigorously established using the Lyapunov method. Several simulations demonstrate the effectiveness of the proposed excitation controller.

关键词： support vector machines Nonlinear control Approximate model Neural networks Identification

Weighted Proximal support vector machines: Robust Classification

在线全文

学校读者我要写书评

暂无评论

Wuhan University Journal of Natural Sciences 2005年第3期10卷 507-510页

作者： ZHANGMeng FULi-hua WANGGao-feng HUJi-cheng SchoolofComputer WuhanUniversityWuhan430072HubeiChina／／AcademyofMicroelectronicsandInformationTechnologyWuhanUniversityWuhan430072HubeiChina DepartmentofMathematicsandPhysics ChinaUniversityofGeosciencesWuhan430074HubeiChina

Despite of its great efficiency for pattern classification, proximal supportvector machines (PSVM), a new version of SVM proposed recently, is sensitive to noise and outliers.To overcome the drawback, this paper modifies PSVM by associating a weightvalue with each input dataof PSVM. The distance between each data point and the center of corresponding class is used tocalculate the weight value. In this way, the effect of noise is reduced. The experiments indicatethat new SVM, weighted proximal support vector machine (WPSVM), is much more robust to noise thanPSVM without loss of computationally attractive feature of PSVM.

关键词： data classification support vector machines linear equation