Abstract
The problem of modeling of nonGaussian processes generated by linear systems driven by a white nonGaussian process, and nonlinear systems driven by a white Gaussian process is addressed using the Volterra representation of systems. Cumulant-based approaches are developed for identifying the parameters of the proposed model when only a finite sample of received observations is available. It is shown that by using a partial set of the output cumulant samples, the computational complexity required in determining the kernels of the model is considerably reduced. The analysis is not restricted to special forms of the second-order Volterra system.< >