On-Line Identification of Nonlinear Systems Using Volterra Polynomial Basis Function Neural Networks
Liu, G.P., Kardikamanathan, V. and Billings, S.A.
(1996)
On-Line Identification of Nonlinear Systems Using Volterra Polynomial Basis Function Neural Networks.
Research Report.
ACSE Research Report 638
.
Department of Automatic Control and Systems Engineering
Abstract
An on-line identification scheme using Volterra polynomial basis function (VPBF) neural networks is considered for nonlinear control systems. This comprises of a structure selection procedure and a recursive weight learning algorithm. The orthogonal least squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in the nonlinear systems. The convergence f both the weights and estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using simulated examples.
Metadata
Item Type:
Monograph
Authors/Creators:
Liu, G.P.
Kardikamanathan, V.
Billings, S.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
Neural networks, Nonlinear system identification; Recursive weighting learning, Growing network, Volterra polynomials, Orthogonal least squares algorithm.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)