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. The error definition in the fuzzy rule extraction features a novel application of the Canberra Distance. The optimal number of rules for identifying the outliers, validated against both artificial and real system datasets, is calculated from the sample of inferred fuzzy models. The optimal number of rules is two in both cases based on the maximum average-error-drop. Using three or more rules results in better error performance; however, the algorithm learns the nuances of the outlier patterns instead. Novel methods for creating the outlier list and determining the optimal number of rules for the outlier detection problem are proposed.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.isatra.2021.01.046DOI Listing

Publication Analysis

Top Keywords

outlier detection
12
optimal number
12
number rules
12
fuzzy rule-based
8
topical negative
8
negative pressure
8
pressure wound
8
wound therapy
8
therapy device
8
input-output dataset
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!