Journal article

Evolving Fuzzy Rules for Anomaly Detection in Data Streams

Masud Moshtaghi, James C Bezdek, Christopher Leckie, Shanika Karunasekera, Marimuthu Palaniswami

IEEE TRANSACTIONS ON FUZZY SYSTEMS | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2015

Abstract

Evolvable Takagi-Sugeno (T-S) models are fuzzy-rule-based models with the ability to continuously learn and adapt to incoming samples from data streams. The model adjusts both premise and consequent parameters to enhance the performance of the model. This paper introduces a new methodology for the estimation of the premise parameters in the evolvable T-S (eTS) model. Incremental updates for the weighted sample mean and inverse of the covariance matrix enable us to construct an evolvable fuzzy rule base that is used to detect outliers and regime changes in the input stream. We compare our model with Angelov's eTS+ model with artificial and real data.

Grants

Funding Acknowledgements

NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.