System identification of wiener systems via volterra-laguerre models: Application to human smooth pursuit analysis

This paper presents novel means for estimating the polynomial static nonlinearity coefficients of a Wiener system in absence of a priori information about the linear block. To capture the system structure, the identification is performed with respect to a Volterra series model, whose kernels are parameterized in terms of Laguerre functions. A property of the resulting Volterra-Laguerre model is exploited to allow for straightforward identification of the coefficients of the output polynomial. The proposed method is shown to provide accurate estimates of the polynomial coefficients even for noisy data. Finally, the method is applied to eye-tracking data obtained to characterize the human smooth pursuit system. The resulting models are evaluated in terms of prediction accuracy and shown to outperform models of previous research.