Identification of Quadratic Volterra System Using Random Multi-Tone Excitation

Abstract

This paper presents a simplified method for the identification of quadratic Volterra systems. Since the higher order kernels often play a secondary role compared to the linear component of the system, it is worth establishing a balance between the calculation consumption of the higher order kernels and their effect on the model accuracy. The equivalent kernels are used to substitute original quadratic kernels, and therefore both the model complexity and identification computational requirement are significantly reduced. Then the identification algorithm based on least square minimization of model output error and random multi-tone excitation is designed. Simulations show this simplified method is of excellent generalization ability and has robustness against noise in output signals.