Fractional Hammerstein CAR system identification

Abstract

Identification of fractional Hammerstein controlled autoregressive systems (HCAR) is considered in this work. This system consists of a memoryless nonlinear sub-system followed by a fractional CAR subsystem. A nonlinear optimization algorithm is developed in order to estimate the system parameters as well as the fractional order. To illustrate the method efficiency, different simulations are performed at various signal to noise ratios.