Relevance of accurate filter design in Hammerstein model identification algorithms of nonlinear systems

The Hammerstein model proved to be very effective in the representation of nonlinear systems. The identification of its kernels is possible through a reliable and computationally light procedure which can be further improved through the use of low- pass filters: this latter technique has been proposed based on the use of FIR filters, which, in the application in object, need a number of coefficients in the order of one thousand. In the present paper we verify the possibility to implement lowpass filtering using IIR structures, which are characterized by a drastically lower number of parameters. In the paper we consider in particular lowpass filters derived from analogue prototypes of Butterworth and Chebyshev types, and we compare their results with those obtainable through the use of FIR filters. The results presented here, verified in an experimental situation, provide the designer of the processing system with useful tools for an optimization of the Hammerstein model identification system.