Utilization of orthogonal higher-order coherence functions for cubic Volterra model identification

Presents an approach to frequency-domain cubic Volterra kernel identification where the kernel has a limited number of significant frequency-domain coefficients (which are complex quantities). The orthogonal higher-order coherence functions are utilized to select the most significant frequency-domain Volterra kernel coefficients to be included in the cubic Volterra model. The practicality and feasibility of this approach is demonstrated by utilizing it to model actual physical nonlinear systems given experimental input-output data from such systems.