Fuzzy rule-based model for outlier detection in a Topical Negative Pressure Wound Therapy Device.

This paper proposes a novel method for offline outlier detection in nonlinear dynamical systems using an input-output dataset of a Topical Negative Pressure Wound Therapy Device, NPWT. The fundamental characteristics of an NPWT describe a chaotic system whose states vary over time and may result in unpredictable and possibly anomalous divergent behavior in the presence of perturbations and other unmodeled system dynamics, despite a quasi-stable controller. Bacterial Memetic Algorithm, BMA, is used to generate fuzzy rule-based models of the input-output dataset.