scholarly journals On-line Support Vector Regression of the transition model for the Kalman filter

2013 ◽  
Vol 31 (6-7) ◽  
pp. 487-501 ◽  
Author(s):  
Samuele Salti ◽  
Luigi Di Stefano
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Support Vector ◽  
Transition Model ◽  
On Line

A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis

2013 ◽  
Vol 228 (6) ◽  
pp. 1048-1062 ◽  
Author(s):  
Chengliang Li ◽  
Zhongsheng Wang ◽  
Shuhui Bu ◽  
Hongkai Jiang ◽  
Zhenbao Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Extend Kalman Filter ◽  
Combining Data ◽  
Novel Method ◽  
Strong Tracking Filter ◽  
Tracking Filter

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.

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.

Turbo-Shaft Engine Model Compensation Based on LSSVR

2012 ◽  
Vol 246-247 ◽  
pp. 738-743
Author(s):  
Feng Lu ◽  
Yi Qiu Lv ◽  
Wei Lin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Support Vector Regression ◽  
Correction Method ◽  
Support Vector ◽  
Simulation Studies ◽  
Model Errors ◽  
Engine Model ◽  
Modeling Errors ◽  
On Line

Considering the larger modeling errors between the turbo-shaft engine and on-board model, the model correction method based on least squares support vector regression is proposed. Firstly, the modeling principle of on-board turbo-shaft engine model is introduced, and then the structure of model combined with a compensation module is designed. The algorithm of LSSVR is used to build up the model compensation module, which is trained off-line and corrected on-line. Simulation studies on turbo-shaft engine have shown that the LS-SVM method can effectively reduce the model errors, and comparison with the interpolation correction, neural network one, the method proposed has better precision.

Fault Prediction for Nonlinear Time Series Based on Temporal Pattern Estimation

2011 ◽  
Vol 121-126 ◽  
pp. 4471-4475
Author(s):  
Sheng Chao Su ◽  
Xiao Lan Fan
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Temporal Pattern ◽  
Least Square ◽  
Fault Prediction ◽  
Training Data ◽  
Support Vector ◽  
Fast Speed ◽  
On Line ◽  
True System

An on-line Least Square Support Vector Regression (LS-SVR) algorithm is given in this paper. Based on this algorithm the method of on-line fault prediction by temporal pattern estimation is proposed. The method needs neither the model to approximate the true system nor the fault training data and primary knowledge. It can study and predict while system’s running, and it is believed with fast speed, fewer amounts of calculation and better real-time capability. The result of simulation on CSTR proved the efficiency.

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