HAMMERSTEIN MODEL IDENTIFICATION BY MULTILAYER FEEDFORWARD NEURAL NETWORKS

Citation
H. Alduwaish et al., HAMMERSTEIN MODEL IDENTIFICATION BY MULTILAYER FEEDFORWARD NEURAL NETWORKS, International Journal of Systems Science, 28(1), 1997, pp. 49-54
Citations number
21
Categorie Soggetti
System Science","Computer Science Theory & Methods","Operatione Research & Management Science
ISSN journal
00207721
Volume
28
Issue
1
Year of publication
1997
Pages
49 - 54
Database
ISI
SICI code
0020-7721(1997)28:1<49:HMIBMF>2.0.ZU;2-N
Abstract
A new method for the identification of the nonlinear Hammer stein mode l, consisting of a static linearity in cascade with a linear dynamic p art, is introduced. The static nonlinearity is modelled by a multilaye r feed forward neural network (MFNN) and the linear part is modelled b y an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the MFNN and the parameters of the ARMA. The new method makes use of the well-known nonlinear mapping ability of MFNN and avoids the restrictive assumptions of the previou s identification methods. Two numerical examples are presented to illu strate the performance of the developed model and recursive algorithm.