期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 23, 期 3, 页码 688-700出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2014.2322385
关键词
Anomaly protection; change detection; evolvable Takagi-Sugeno (T-S) model; evolving fuzzy systems; pattern recognition; streaming data
资金
- Australian Government as represented by the Department of Broadband, Communications and the Digital Economy
- Australian Research Council through the ICT Centre of Excellence program
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据