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

Abstract

This paper investigates the use of particle swarm optimization (PSO) in the identification of Hammerstein models with known nonlinearity structure. The parameters of the Hammerstein model are estimated using PSO from the input–output data by minimizing the error between the true model output and the identified model output. Using PSO, 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.