scholarly journals Comparison Between Conventional Methods, Neural Network, and Support Vector Regression in Forecasting Foreign Tourists in Indonesia

2021 ◽  
Vol 1125 (1) ◽  
pp. 012056
Author(s):  
Purwati ◽  
Donny Montreano ◽  
Alina Cynthia Dewi
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Support Vector ◽  
Conventional Methods

Comparisons of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper

2011 ◽  
Vol 403-408 ◽  
pp. 3805-3812 ◽  
Author(s):  
Kong Hui Guo ◽  
Xian Yun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Support Vector Regression ◽  
Network Models ◽  
Support Vector ◽  
Neural Network Models ◽  
Hydraulic Damper ◽  
Nonparametric Models ◽  
Force Velocity ◽  
Bpnn Model

Nonparametric models of hydraulic damper based on support vector regression (SVR) are developed. Then these models are compared with two kinds neural network models. One is backpropagation neural network (BPNN) model; another is radial basis function neural network (RBFNN) model. Comparisons are carried out both on virtual damper and actual damper. The force-velocity relation of a virtual damper is obtained based on a rheological model. Then these data are used to identify the characteristics of the virtual damper. The dynamometer measurements of an actual displacement-dependent damper are obtained by experiment. And these data are used to identify the characteristics of this actual damper. The comparisons show that BPNN model is best at identifying the characteristics of the virtual damper, but SVR model is best at identifying the characteristics of the actual damper. The reason is that all experimental data include noise more or less. When the amplitude of the noise is smaller than the parameter of SVR, the noise can not affect the construction of the resulting model. So when training a model based on the experimental data, SVR is superior to other neural networks methods.

Dimensional Prediction for FDM Machines Using Artificial Neural Network and Support Vector Regression

2019 ◽  
Author(s):  
Jiaqi Lyu ◽  
Souran Manoochehri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Multivariate Linear Regression ◽  
Support Vector ◽  
Ann Model ◽  
Fused Deposition ◽  
Artificial Neural

Abstract With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.

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