Non-parametric orthogonal series identification of Hammerstein systems

Abstract

The non-linearity in a discrete system governed by the Hammerstein functional is identified. The system is driven by a random while input signal and the output is disturbed by a random white noise. No parametric a priori information concerning the non-linearity is available and non-parametric algorithms are proposed. The algorithms are derived from the trigonometric as well as Hermite orthogonal series. It is shown that the algorithms converge to the unknown characteristic in a pointwise manner and that the mean integrated square error converges to zero as the number of observations tends to infinity. The rate of convergence is examined. A numerical example is also given.