scholarly journals A prediction method of missing vehicle position information based on least square support vector machine

2021 ◽  
Author(s):  
Peng DU ◽  
Xiaoqi MA ◽  
Zhuanping WANG ◽  
Yuanfu MO ◽  
Peng PENG
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector ◽  
Position Information ◽  
Vehicle Position

Research on Prediction Method of Soft Soil Foundation Settlement

2011 ◽  
Vol 97-98 ◽  
pp. 36-39
Author(s):  
Xiao Ma Dong
Keyword(s):  
Support Vector Machine ◽  
Soft Soil ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector ◽  
Prediction Methods ◽  
Foundation Settlement ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Curve Fitting Method

The current prediction methods of foundation settlement have biggish error under the condition of lesser foundation settlement observational datum. Aim at the localization of present prediction methods and the virtues of Support Vector Machine arithmetic, the method of predicting soft soil foundation settlement based on Least Square Support Vector Machine (LS-SVM) was proposed in this paper and compared with the neural network method and curve fitting method. The research results show that this proposed method is feasible and effective for predicting soft soil foundation settlement. Least Square Support Vector Machine provides a more advanced method than these conventional methods for predicting foundation settlement.

A prediction method for deck-motion based on online least square support vector machine and genetic algorithm

2018 ◽  
Vol 24 (2) ◽  
pp. 382-397
Author(s):  
Xixiang Liu ◽  
Qiming Wang ◽  
Rong Huang ◽  
Songbing Wang ◽  
Xianjun Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector

Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 3333-3337
Author(s):  
Shuang Shuang Yu ◽  
Tie Ning Wang ◽  
Ning Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Original Data ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Grey Prediction ◽  
Demand Prediction ◽  
Efficient Prediction ◽  
General Demand

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

Trend Prediction of Hydraulic Liquid Leakage Based on ES-LSSVM

2011 ◽  
Vol 137 ◽  
pp. 440-444 ◽  
Author(s):  
Zhi Yong Wu ◽  
Zeng Bing Xu ◽  
Ming Gao
Keyword(s):  
Support Vector Machine ◽  
Hydraulic System ◽  
Prediction Method ◽  
Least Square ◽  
Fault Prediction ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Short Term ◽  
Trend Prediction ◽  
Internal Parameters

A novel prediction method which combined evolutionary strategy with least-square support vector machine is presented and applied to the trend prediction of hydraulic liquid leakage in this paper. In order to improve the prediction performance, the evolutionary strategy is employed to optimize the internal parameters of least-square support vector machine. Through the experiment study, the result validated the effectiveness of the prediction method, and it is also demonstrated that the method is able to do the short-term fault prediction for the hydraulic system.

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