Identification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks

Abstract

Proposes a recursive identification technique for nonlinear discrete-time multivariable dynamical systems. Extending an earlier result of the authors (1993) to multivariable systems, the technique approaches a nonlinear system identification problem in two stages: one is to build up recursively a RBF (radial-basis-function) neural net model structure including the size of the neural net and the parameters in the RBF neurons; the other is to design a stable recursive weight updating algorithm to obtain the weights of the net in an efficient way. >