On the identification of Wiener systems with polynomial nonlinearity

In this paper we introduce a new method for Wiener system identification that relies on the data collected on two separate experiments. In the first experiment, the system is excited with a sine signal at fixed frequency and phase shift. Using the steady state response of the system, we estimate the static nonlinearity, which is assumed to be a polynomial. In the second experiment, the system is fed with a persistently exciting input, which allows to identify the linear time-invariant block composing the Wiener structure. We show that the estimation of the static nonlinearity reduces to the solution of a least squares problem, and we provide an expression for the asymptotic variance of the estimated polynomial coefficients. The effectiveness of the method is demonstrated through numerical experiments

On the identification of Wiener systems with polynomial nonlinearity

© 2017 IEEE. In this paper we introduce a new method for Wiener system identification that relies on the data collected on two separate experiments. In the first experiment, the system is excited with a sine signal at fixed frequency and phase shift. Using the steady state response of the system, we estimate the static nonlinearity, which is assumed to be a polynomial. In the second experiment, the system is fed with a persistently exciting input, which allows to identify the linear time-invariant block composing the Wiener structure. We show that the estimation of the static nonlinearity reduces to the solution of a least squares problem, and we provide an expression for the asymptotic variance of the estimated polynomial coefficients. The effectiveness of the method is demonstrated through numerical experiments.status: publishe

On the identification of Wiener systems with polynomial nonlinearity

In this paper we introduce a new method for Wiener system identification that relies on the data collected on two separate experiments. In the first experiment, the system is excited with a sine signal at fixed frequency and phase shift. Using the steady state response of the system, we estimate the static nonlinearity, which is assumed to be a polynomial. In the second experiment, the system is fed with a persistently exciting input, which allows to identify the linear time-invariant block composing the Wiener structure. We show that the estimation of the static nonlinearity reduces to the solution of a least squares problem, and we provide an expression for the asymptotic variance of the estimated polynomial coefficients. The effectiveness of the method is demonstrated through numerical experiments