Hammerstein model identification using radial basis functions neural networks
| Author(s) : | SSA Ali HN Al-Duwaish, Ali SSA Al-Duwaish HN, |
| Publisher : | SPRINGER-VERLAG BERLIN |
| Publication Date : | 2001 |
| ISSN : | N/A |
| Abstract : | A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model., |