NARX-based nonlinear system identification using orthogonal least squares basis hunting
Chen, S., Wang, X.X. and Harris, C.J. (2008) NARX-based nonlinear system identification using orthogonal least squares basis hunting. IEEE Transactions on Control Systems Technology, 16, (1), 78-84. (doi:10.1109/TCST.2007.899728).
Available under License Creative Commons Public Domain Dedication.
An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, which places the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method is adopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||02 Jan 2008 09:14|
|Last Modified:||27 Mar 2014 20:09|
|Further Information:||Google Scholar|
|ISI Citation Count:||16|
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