Parsimonious Volterra System Identification

TL;DR

This paper develops algorithms for identifying sparse Volterra systems with infinite impulse responses, focusing on models with minimal exponential components, and demonstrates their effectiveness with noisy and fragmented data.

Contribution

It introduces novel algorithms for sparse Volterra system identification that handle infinite impulse responses and noisy, fragmented data.

Findings

Algorithms successfully identify minimal exponential models.

Effective in noisy measurement scenarios.

Validated with academic examples.

Abstract

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.