Multi-scale Combination Prediction Model with Least Square Support Vector Machine for Network Traffic

2005 ◽  
pp. 385-390 ◽  
Author(s):  
Zunxiong Liu ◽  
Deyun Zhang ◽  
Huichuan Liao
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Network Traffic ◽  
Least Square ◽  
Support Vector ◽  
Multi Scale

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

Optimization of Financial Forecasting Based on Least Squares Support Vector Machine

2013 ◽  
Vol 760-762 ◽  
pp. 1987-1991
Author(s):  
Yun Fa Li
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Stock Market ◽  
Prediction Model ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Stock Investment ◽  
Annealing Algorithm

To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.

scholarly journals Research on Monthly Precipitation Prediction Based on the Least Square Support Vector Machine with Multi-Factor Integration

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1076
Author(s):  
Jingchun Lei ◽  
Quan Quan ◽  
Pingzhi Li ◽  
Denghua Yan
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Flood Control ◽  
Historical Data ◽  
Yellow River ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Precipitation Prediction

Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA). The model was used to estimate the precipitation of each meteorological station over the source region of the Yellow River (SRYE) in China for 12 months. The Ensemble empirical mode decomposition (EEMD) method was used to select meteorological factors and realize precipitation prediction, without dependence on historical data as a training set. The prediction results were compared with each other, according to the determination coefficient (R2), mean absolute errors (MAE), and root mean square error (RMSE). The results show that sea surface temperature (SST) in the Niño 1 + 2 region exerts the largest influence on accuracy of the prediction model for precipitation in the SRYE (RSST2= 0.856, RMSESST= 19.648, MAESST= 14.363). It is followed by the potential energy of gravity waves (Ep) and temperature (T) that have similar effects on precipitation prediction. The prediction accuracy is sensitive to altitude influences and accurate prediction results are easily obtained at high altitudes. This model provides a new and reliable research method for precipitation prediction in regions without historical data.

A Prediction Model for Surface Roughness in Milling Based on Projection Pursuit Regression

2013 ◽  
Vol 312 ◽  
pp. 143-147
Author(s):  
Xue Qin Pan ◽  
Na Zhu ◽  
Yu Cai Dong ◽  
Cui Xiang Liu ◽  
Min Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Projection Pursuit ◽  
Least Square ◽  
Support Vector ◽  
Prediction Errors ◽  
Machined Surface ◽  
Projection Pursuit Regression ◽  
Milling Parameters

A novel prediction model for surface roughness based on Projection Pursuit Regression was proposed in this paper. Based on the new model, the effects of milling parameters on surface roughness in milling can be predicted, and the predicted value of surface roughness in the whole working range can be reached with the limited test data, thus the variation law of quality of machined surface following milling parameters can be obtained. Compared with the least square support vector machine, it can be revealed that on the base of the same samples, the construction speed of this Projection Pursuit Regression is 1~2 higher in order of magnitude than that of the least square support vector machine, while the prediction errors are 40 % of the latter. Thus, the prediction model based on Projection Pursuit Regression can be established fast and be forecasted in high-precision, it is suitable for prediction of surface roughness.

Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM)

2018 ◽  
Vol 137 ◽  
pp. 686-712 ◽  
Author(s):  
Lov Kumar ◽  
Sai Krishna Sripada ◽  
Ashish Sureka ◽  
Santanu Ku. Rath
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector
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