A Fast Grid Search Method in Support Vector Regression Forecasting Time Series

2006 ◽  
pp. 504-511 ◽  
Author(s):  
Yukun Bao ◽  
Zhitao Liu
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Grid Search Method

scholarly journals Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hong Zhang ◽  
Lixing Chen ◽  
Yong Qu ◽  
Guo Zhao ◽  
Zhenwei Guo
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Real Data ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Grid Search Method ◽  
Power Forecasting

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

scholarly journals Estimating Concrete Workability Based on Slump Test with Least Squares Support Vector Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Search Method ◽  
Support Vector ◽  
Dam Construction ◽  
Grid Search ◽  
Hydroelectric Dam ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Grid Search Method

Concrete workability, quantified by concrete slump, is an important property of a concrete mixture. Concrete slump is generally known to affect the consistency, flowability, pumpability, compactibility, and harshness of a concrete mix. Hence, an accurate prediction of this property is a practical need of construction engineers. This research proposes a machine learning model for predicting concrete slump based on the Least Squares Support Vector Regression (LS-SVR). LS-SVR is employed to model the nonlinear mapping between the mix components and slump values. Since the learning process of the LS-SVR necessitates two hyperparameters, the regularization and the kernel parameters, the grid search method is employed search for the most desirable set of hyperparameters. Furthermore, to construct the hybrid model, this research collected a dataset including actual concrete slump tests from a hydroelectric dam construction project in Vietnam. Experimental results show that the proposed model is capable of predicting concrete slump accurately.

scholarly journals Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

2021 ◽  
Vol 7 ◽  
pp. e417
Author(s):  
Xinyu Liu ◽  
Peiwen Hao ◽  
Aihui Wang ◽  
Liangqi Zhang ◽  
Bo Gu ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Local Maxima ◽  
Grid Search Method ◽  
Hidden Layer ◽  
Ground Penetrating

In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM classification algorithm was used for condition classification. For parameter optimization of the SVM algorithm, the grid search method and particle swarm optimization algorithm were used. The recognition rate using the grid search method was 88.333%; the PSO approach often yielded local maxima, and the recognition rate was 86.667%; the improved adaptive PSO algorithm avoided local maxima and increased the recognition rate to 91.667%.

scholarly journals Peramalan Jumlah Penumpang Datang Melalui Transportasi Udara Di Sulawesi Tengah Menggunakan Support Vector Regression (SVR)

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
D I Purnama
Keyword(s):  
Air Transportation ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Optimal Parameters ◽  
Forecasting Method ◽  
Grid Search Method ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Central Sulawesi

The number of air transportation passengers in Central Sulawesi shows an increase and decrease every month. For this reason, a forecasting method is needed to predict the number of air transportation passengers in the future. Because the pattern of data on the number of air transportation passengers in Central Sulawesi Province has a nonlinear data pattern, a forecasting method is needed that can overcome these problems where in this study using the SVR model. In this study, the SVR model uses the RBF kernel function to overcome nonlinear data patterns and uses the grid search method to obtain the optimal parameters of the model.

scholarly journals Performance of the pilling evaluation method based on the technique of DFF

2017 ◽  
Vol 68 (01) ◽  
pp. 13-16 ◽  
Author(s):  
YU LingJie ◽  
WANG RongWu ◽  
ZHOU JinFeng
Keyword(s):  
Evaluation Method ◽  
Optimal Parameter ◽  
Search Method ◽  
Depth Image ◽  
Support Vector ◽  
Large Set ◽  
Grid Search ◽  
Segmented Image ◽  
Grid Search Method ◽  
Parameter Values

In previous work, we reconstructed the depth image of fabric based on the method of Depth from Focus (DFF) and segmented pills and fuzz from fabric background. Work in this paper was performed using the segmented image. Here, we demonstrate the prediction operation of the pilling evaluation using a large set of fabric samples. The support vector machine (SVM) was applied to build the classifier machine by learning from existing data. The grid search method was used to select the optimal parameter values. The study found that the best prediction accuracy can reach 90.75%, indicating the extracted pilling features from depth image can predict the pilling grade well.

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