System Identification Based on Hammerstein Models Using Cubic Splines
Nonlinear system modeling plays an important role in the field of digital audio systems whereas most of the real-world devices show a nonlinear behavior. Among nonlinear models, Hammerstein systems are particular nonlinear systems composed of a static nonlinearity cascaded with a linear filter. In this paper a novel approach for the estimation of the static nonlinearity is proposed based on the introduction of an adaptive CatmullRom cubic spline in order to overcome problems related to the adaptation of high-order polynomials necessary for identifying highly nonlinear systems. Experimental results confirm the effectiveness of the approach, making also comparisons with existing techniques of the state of the art.
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