Fuzzy rule-based model for outlier detection in a topical negative pressure wound therapy device
Research output: Contribution to journal › Article › peer-review
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.
|Publication status||Accepted for publication - 28 Jan 2021|
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Accepted author manuscript (Post-print), 5.52 MB, PDF document
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