Identification of Hammerstein CARMA systems with scarce measurements based on PSO and auxiliary model

This paper investigates the identification of Hammerstein CARMA systems with scarce measurements. The auxiliary model (AM) method is employed to solve the incomplete data problem caused by scarce measurements. And the particle swarm optimization (PSO) is used to estimate the parameters of the Hammerstein CARMA systems. In order to improve the convergence speed and the identification accuracy, some improvement strategies have been applied to the standard PSO algorithm. Then, the auxiliary model-based particle swarm optimization (AM-PSO) identification algorithm is proposed. The simulation results show that the derived AM-PSO algorithm can identify the Hammerstein CARMA systems with scarce measurements effectively.