Support Vector Regression for Black-box System Identification
نویسندگان
چکیده
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydraulic robot arm, and show that SVR improves on previously published results.
منابع مشابه
Identification of an univariate function in a nonlinear dynamical model
This paper addresses the problem of estimating, from measurement data corrupted by highly correlated noise, the shape of an unknown scalar and univariate function hidden in a known phenomenological model of the system. The method makes use of the Vapnik’s support vector regression to find the structure of a parametrized black box model of the unknown function. Then the parameters of the black b...
متن کاملidentification modelling of ship manoeuvring motion based on - ε support vector regression
Based on the ε support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The o o 10 /10 , o o 20 / 20 zigzag tests and the o 35 turning circle manoeuvre are simulated. Part of the simulation data for the o o 20 / 20 zigzag test are used to train the support vectors, and...
متن کاملNon-Mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification
As a new sparse kernelmodelingmethod, support vector regression (SVR) has been regarded as the stateof-the-art technique for regression and approximation. In [V.N. Vapnik, The Nature of Statistical Learning Theory, second ed., Springer-Verlag, 2000], Vapnik developed the e-insensitive loss function for the support vector regression as a trade-off between the robust loss function of Huber and on...
متن کاملModel Structure Determination and Identification with Kernel Based Partially Linear Models
In nonlinear system identification, the black-box approach can often be applied successfully. However, if we know a priori that some part of the model is linear, then the estimation of a partially linear model can produce better identification results. In this paper, by using Least Squares Support Vector Machines (LS-SVMs) as a black-box technique, and the proposed kernel-based partially linear...
متن کاملNomograms for Visualizing Linear Support Vector Machines
Support vector machines are often considered to be black box learning algorithms. We show that for linear kernels it is possible to open this box and visually depict the content of the SVM classifier in high-dimensional space in the interactive format of a nomogram. We provide a crosscalibration method for obtaining probabilistic predictions from any SVM classifier, which control for the genera...
متن کامل