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Identification of Hammerstein Model with Known Nonlinearity Structure Using Particle Swarm Optimization
 

Summary: Identification of Hammerstein Model with Known Nonlinearity Structure
Using Particle Swarm Optimization
Hussain N. Al-Duwaish
Department of Electrical Engineering
King Fahd University of Petroleum and Minerals
Dhahran 31261
Saudi Arabia.
E-mail: hduwaish@dpc.kfupm.edu.sa
Keywords: Nonlinear System Identification, Hammerstein Model, Particle Swarm
Optimization
Abstract
This paper investigates the use of particle swarm optimization in the identification of
Hammerstein model with known nonlinearity structure. The parameters of the Hammerstein
model are estimated using particle swarm optimization from the input-output data by
minimizing the error between the true model output and the identified model output. Using
particle swarm optimization, Hammerstein models with known nonlinearity structure and
unknown parameters can be identified. Moreover, systems with non-minimum phase
characteristics can be identified. Extensive simulations have been used to study the
convergence properties of the proposed scheme. Simulation examples are included to
demonstrate the effectiveness and robustness of the proposed identification scheme.

  

Source: Al-Duwaish, Hussain N. - Electrical Engineering Department, King Fahd University of Petroleum and Minerals

 

Collections: Computer Technologies and Information Sciences; Engineering