Using spline basis functions in Volterra series based models

The choice of nonlinear description used in Volterra based models strongly influences the robustness of the parameter's extraction. Traditionally, monomials are used as the nonlinear description. However, it has been shown that other polynomials provide easier extraction. Volterra-based models can also be described using spline interpolated look-up tables (LUTs). Splines have increased locality which improves conditioning, converging to polynomials as the spline order-1 tends to the number of LUT points. In this paper we first show how to describe Volterra models in terms of splines and then study the conditioning and error for increasing spline order in a practical example.