Wavelet Support Vector Machines and Its Application for Nonlinear System Identification

2005 ◽  
pp. 442-447 ◽  
Author(s):  
Xiangjun Wen ◽  
Yunze Cai ◽  
Xiaoming Xu
Keyword(s):  
Support Vector Machines ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Vector Machines

scholarly journals Identification of a Surface Marine Vessel Using LS-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
David Moreno-Salinas ◽  
Dictino Chaos ◽  
Jesús Manuel de la Cruz ◽  
Joaquín Aranda
Keyword(s):  
Experimental Data ◽  
Support Vector Machines ◽  
System Identification ◽  
Mathematical Models ◽  
Nonlinear System Identification ◽  
Experimental Tests ◽  
Support Vector ◽  
Experimental Systems ◽  
Research Areas ◽  
Vector Machines

The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to define mathematical models from experimental data. Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability is demonstrated through the identification of the Nomoto second-order ship model with real experimental data obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model obtained is validated through experimental tests that illustrate the potential of SVM for system identification.

scholarly journals Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lan Wang ◽  
Yu Cheng ◽  
Jinglu Hu ◽  
Jinling Liang ◽  
Abdullah M. Dobaie
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Radial Basis Function Network ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Identification Procedure ◽  
Identification Methods ◽  
Nonlinear Parameter Identification ◽  
And Control ◽  
Function Network

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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