Online probabilistic learning for fuzzy inference system
作者：
Highlights：
• We develop a probability-based online learning method for fuzzy inference systems.
• Our online learning is built upon the statistical concept of maximum a posteriori.
• Experiments show that our method can balance between model accuracy and simplicity.
• Empirical results show our model can handle large and nonstationary data stream.
摘要
•We develop a probability-based online learning method for fuzzy inference systems.•Our online learning is built upon the statistical concept of maximum a posteriori.•Experiments show that our method can balance between model accuracy and simplicity.•Empirical results show our model can handle large and nonstationary data stream.
论文关键词：Adaptive resonance theory,Bayes’ rule,Kalman filter,Neuro-fuzzy system,Online learning
论文评审过程：Available online 19 February 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.01.034
