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A new Subspace based method for identification of Hammerstein models
 

Summary: A new Subspace based method for
identification of Hammerstein models
H. N. Al-Duwaish
, and S. Z. Rizvi
P.O.Box (677 ,76 ), Department of Electrical Engineering,
King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
email:{hduwaish ,srizvi }@kfupm.edu.sa
Abstract
This paper presents a new identification method for Hammerstein models. The
developed identification model uses state space model in cascade with a radial basis
functions neural network. A recursive algorithm is developed for estimating the
weights of the neural network and the parameters of the state space model. No
assumption on the structure of nonlinearity is made. The proposed algorithm works
under the weak assumption of richness of inputs. Simulation examples are included
to illustrate the performance of proposed algorithm.
Keywords: Hammerstein; least mean square; subspace identification; radial
basis functions; static nonlinearity, dynamic linearity

Corresponding author: Tel +966 55 834 1826, Fax +966 3 860 3535
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Source: Al-Duwaish, Hussain N. - Electrical Engineering Department, King Fahd University of Petroleum and Minerals

 

Collections: Computer Technologies and Information Sciences; Engineering