Identification of nonlinear discrete-time systems using raised-cosine radial basis function networks

Authors:
A. F. Al-Ajlouni;R. J. Schilling;S. L. Harris
Affiliations:
Hijjawi Faculty of Engineering Technology, Department of Communication Engineering, Yarmouk University, Irbid, Jordan;Electrical and Computer Engineering Department, Clarkson University, Potsdam, NY;Chemical Engineering Department, Clarkson University, Potsdam, NY
Venue:
International Journal of Systems Science
Year:
2004

Citing 9
Cited 1

Adaptive signal processing

Adaptive signal processing
Radial basis function approximations to polynomials

Numerical analysis 1987
A resource-allocating network for function interpolation

Neural Computation
A function estimation approach to sequential learning with neural networks

Neural Computation
A digital signal processing primer, with applications to digital audio and computer music

A digital signal processing primer, with applications to digital audio and computer music
Stability of nonlinear polynomial ARMA models and their inverse

IBM Journal of Research and Development
Robust radial basis function neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approximation of nonlinear systems with radial basis function neural networks

IEEE Transactions on Neural Networks
On the persistency of excitation in radial basis function network identification of nonlinear systems

IEEE Transactions on Neural Networks

Learning in fully recurrent neural networks by approaching tangent planes to constraint surfaces

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

An effective technique for identifying nonlinear discrete-time systems using raised-cosine radial basis function (RBF) networks is presented. Raised-cosine RBF networks are bounded-input bounded-output stable systems, and the network output is a continuously differentiable function of the past input and the past output. The evaluation speed of an n-dimensional raised-cosine RBF network is high because, at each discrete time, at most 2n RBF terms are nonzero and contribute to the output. As a consequence, raised-cosine RBF networks can be used to identify relatively high-order nonlinear discrete-time systems. Unlike the most commonly used RBFs, the raised-cosine RBF satisfies a constant interpolation property. This makes raised-cosine RBF highly suitable Jor identifying nonlinear systems that undergo saturation effects. In addition, Jar the important special case of a linear discrete-time system, a first-order raised-cosine RBF network is exact on the domain over which it is defined, and it is minimal in terms of the number of distinct parameters that must be stored. Several examples, including both physical systems and benchmark systems, are used to illustrate that raised-cosine RBF networks are highly effective in identifying nonlinear discrete-time systems.