A Time-Domain Fractional Approach for Wiener-Hammerstein Systems Identification

This paper describes a new approach to initialize Wiener-Hammerstein models for iterative prediction error minimization. The key idea is to parameterize the division of poles and zeros of the best linear approximation (BLA), between the two linear subsystems. Taylor expansion is used to handle the parameterization in the time-domain. Identifiability aspects are investigated on a low order example. Results regarding uniqueness of the estimation are proved. The initial estimate generated with this approach allows to avoid problems with local minima and the identification problem is solved performing only two iterative optimizations.