Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2014
Abstract
Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended Kalman Filter (RDEKF) to generate the rule base structure and refine its parameters, respectively. The marriage of the BART and RDEKF methods, both of which are built upon the maximum a posteriori (MAP) principle rooted in the Bayes' rule, offers a comprehensive probabilistic treatment and an efficient way for online structural and parameter learning suitable for large, dynamic data stream. To manage the model complexity without sacrificing its predictive accuracy, SPLAFIS also includes a simple procedure to prune inconsequential rules that have little contribution overtime. The predictive accuracy, structural simplicity, and scalability of the proposed model have been exemplified in empirical studies using chaotic time series, stock index, and large nonlinear regression datasets.
Keywords
Adaptive resonance theory, Bayes' rule, Kalman filter, Neuro-fuzzy system, Online learning
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Expert Systems with Applications
Volume
41
Issue
11
First Page
5082
Last Page
5096
ISSN
0957-4174
Identifier
10.1016/j.eswa.2014.01.034
Publisher
Elsevier
Citation
OENTARYO, Richard Jayadi; ER, Meng Joo; LINN, San; and LI, Xiang.
Online probabilistic learning for fuzzy inference system. (2014). Expert Systems with Applications. 41, (11), 5082-5096.
Available at: https://ink.library.smu.edu.sg/sis_research/3250
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.eswa.2014.01.034
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons