An enhanced extrapolation method based on particle swarm optimization-support vector regression to determine the friction coefficient between aircraft tire and runway surface

2019 ◽  
Vol 90 (9) ◽  
pp. 095110
Author(s):  
Liwei Zhan ◽  
Fang Ma ◽  
Xiaojun Song ◽  
Hongwei Sun ◽  
Chengwei Li
Keyword(s):  
Particle Swarm Optimization ◽  
Friction Coefficient ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Extrapolation Method ◽  
Support Vector ◽  
Swarm Optimization

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

2016 ◽  
Vol 25 (8) ◽  
pp. 1248-1258 ◽  
Author(s):  
Fayçal Megri ◽  
Ahmed Cherif Megri ◽  
Riadh Djabri
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Support Vector Regression ◽  
Thermal Sensation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Statistical Regression ◽  
Swarm Optimization ◽  
Comfort Indices

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

2010 ◽  
Vol 09 (02) ◽  
pp. 87-104 ◽  
Author(s):  
JIANSHENG WU ◽  
MINGZHE LIU ◽  
LONG JIN
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Support Vector Regression ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Support Vector Regression Model ◽  
Swarm Optimization

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application.

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