Báo Cáo Nonlinear system identification using radial basis function neural networks

ABSTRACT
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This paper considers the problem of nonlinear dynamical system identification using radial basis
function neural networks. A recursive algorithm is proposed for simultaneously identifying nonlinear
and linear parameters. The convergence of the algorithm is investigated. An Akaike like criterion for
model structure selection is introduced. Simulation results are presented.

Index terms – Identification, Nonlinear System, Parameter Estimation, Radial Basis Function (RBF),
RBF Neural Networks.