Continuous-time Hammerstein system identification from sampled data

A continuous-time Hammerstein system driven by a random signal is identified from observations sampled in time. The sampling may be uniform or not. The a-priori information about the system is nonparametric, functional forms of both the nonlinear characteristic and the impulse response are completely unknown. Three kernel algorithms, one offline and two semirecursive are presented. Their convergence to the true characteristic of the nonlinear subsystem is shown. The distance between consecutive sampling times must not decrease too fast for the algorithms to converge.