Identification of dynamic nonlinear polynomial models in the errors-in-variables framework

Abstract

An approach for the identification of a class of discrete-time nonlinear polynomial single-input single-output dynamic errors-in-variables system models in the case of white input noise and white output noise is proposed. The algorithm is constructed within the extended bias compensated least squares framework. The separable nonlinear least squares method is subsequently utilised to determine the model parameters together with the input and output noise variances. A numerical Monte-Carlo simulation demonstrates the robustness of the proposed approach with regard to noise.