Details

Autor(en) / Beteiligte

• Azhar, Ali Syed Saad
• Al-Duwaish, Hussain N.
• Dorronsoro, José R.

Titel

Identification of Wiener Model Using Radial Basis Functions Neural Networks

Ist Teil von

• Artificial Neural Networks — ICANN 2002, 2002, p.344-350

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2002

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• A new method is introduced for the identification of Wiener model. The Wiener model consists of a linear dynamic block followed by a static nonlinearity. The nonlinearity and the linear dynamic part in the model are identified by using radial basis functions neural network (RBFNN) and autoregressive moving average (ARMA) model, respectively. The new algorithm makes use of the well known mapping ability of RBFNN. The learning algorithm based on least mean squares (LMS) principle is derived for the training of the identification scheme. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model simultaneously.