Research of Bessel Kernel Function of the First Kind for Support Vector Regression

2013 ◽  
Vol 12 (14) ◽  
pp. 2673-2682 ◽  
Author(s):  
Li Xiang ◽  
Zhu Quanyin ◽  
Wang Liuyang
Keyword(s):  
Support Vector Regression ◽  
Kernel Function ◽  
Support Vector ◽  
Bessel Kernel

scholarly journals Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

2018 ◽  
Vol 8 (5) ◽  
pp. 3341 ◽  
Author(s):  
Edy Fradinata ◽  
Sakesun Suthummanon ◽  
Wannarat Suntiamorntut
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Activation Function ◽  
Support Vector ◽  
Optimal Parameters ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Linear Loss

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.

scholarly journals Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hailun Wang ◽  
Daxing Xu
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Unscented Kalman Filter ◽  
Selection Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Regression Parameters ◽  
Cubature Kalman Filter ◽  
Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

scholarly journals Prior-knowledge based Green's kernel for support vector regression

2021 ◽  
Author(s):  
Tahir Farooq
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Kernel Function ◽  
Real World ◽  
Matched Filter ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Model Complex

This thesis presents a novel prior knowledge based Green's kernel for support vector regression (SVR) and provides an empirical investigation of SVM's (support vector machines) ability to model complex real world problems using a real dataset. After reviewing the theoretical background such as theory SVM, the correspondence between kernels functions used in SVM and regularization operators used in regularization networks as well as the use of Green's function of their corresponding regularization operators to construct kernel functions for SVM, a mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization and also makes it suitable for signals corrupted with noise that includes many real world systems. Several experiments, mostly using benchmark datasets ranging from simple regression models to non-linear and high dimensional chaotic time series, have been conducted in order to compare the performance of the proposed technique with the results already published in the literature for other existing support vector kernels over a variety of settings including different noise levels, noise models, loss functions and SVM variations. The proposed kernel function improves the best known results by 18.6% and 24.4% on a benchmark dataset for two different experimental settings.

State estimation for the electro-hydraulic actuator based on particle filter with an improved resampling technique

2019 ◽  
Vol 234 (1) ◽  
pp. 41-51
Author(s):  
Runxia Guo ◽  
Zhile Wei ◽  
Ye Wei
Keyword(s):  
Probability Density Function ◽  
State Estimation ◽  
Support Vector Regression ◽  
Probability Density ◽  
Density Function ◽  
Kernel Function ◽  
Particle Filters ◽  
Support Vector ◽  
Hydraulic Actuator ◽  
Support Vector Regression Model

State estimation for the electro-hydraulic actuator of civil aircraft is one of the most valuable but intractable issues. Recently, the state estimation approach based on particle filters has widely attracted attention. We pursue the benefits of the data-driven approach when physical model is deficienct, and put forward some improvements that are triggered by the shortcomings of particle filters algorithm. In order to solve the particles’ degeneracy phenomenon in particle filters, a kernel function that integrates the information of probability distribution is constructed; then, the established probability kernel function is designed to represent the probability density function of resampling and the regularization form of probability density function in Hilbert space is defined. Consequently, the probability density function of resampling is obtained by solving the support vector regression model. The novel resampling method based on support vector regression-particle filters can keep the diversity of particles as well as relieve the degeneracy phenomenon and eventually make the estimated state more realistic. The approach is simulated and applied to an electro-hydraulic actuator model. The estimation results validate the effectiveness of the proposed algorithm.

scholarly journals Shear force estimation in rough boundaries using SVR method

2019 ◽  
Vol 9 (8) ◽  
Author(s):  
Zohreh Sheikh Khozani ◽  
Hossein Hosseinjanzadeh ◽  
Wan Hanna Melini Wan Mohtar
Keyword(s):  
Support Vector Regression ◽  
Kernel Function ◽  
Shear Force ◽  
Rectangular Channel ◽  
The Other ◽  
Support Vector ◽  
Force Estimation ◽  
Exponential Kernel ◽  
Input Combination ◽  
Rough Boundaries

Abstract The accuracy of support vector regression (SVR) procedure in modeling the percentage of shear force carried by walls in a rectangular channel with rough boundaries was investigated. The SVR model is extended, and the more appropriate kernel function and input combination are studied. Finally, the SVR model with an exponential kernel function and three influence parameters was selected as the best SVR model with the lowest error. The output of this more appropriate SVR model is presented as a program. Then, this most appropriate SVR model is compared with three equations presented by other researchers for rough and smooth channels. The SVR model with the highest accuracy and lowest statistical values (RMSE of 0.565) performed the best compared with the other equations.

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