  In this short paper, we aim at developing algorithms for sparse Volterra
system identification when the system to be identified has infinite impulse
response. Assuming that the impulse response is represented as a sum of
exponentials and given input-output data, the problem of interest is to find
the "simplest" nonlinear Volterra model which is compatible with the a priori
information and the collected data. By simplest, we mean the model whose
impulse response has the least number of exponentials. The algorithms provided
are able to handle both fragmented data and measurement noise. Academic
examples at the end of paper show the efficacy of proposed approach.
