Estimation errors of volterra kernels measured by use of nonwhite input

Abstract

It is well known that employment of nonwhite input in system identification often causes large estimation errors. While the mechanism of inducing these errors has been well understood for linear system identification, it has not been made clear yet for nonlinear systems. This paper analyzes this mechanism for nonlinear system identification with Volterra functional series models. A vector space approach gives a clear physical interpretation of the error generation from a viewpoint of the excitation intensity of the input against the identified system. Based on this analysis, new algorithms of reducing estimation errors are proposed.