Online probabilistic learning for fuzzy inference system

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 cur...

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Main Authors: OENTARYO, Richard Jayadi, ER, Meng Joo, LINN, San, LI, Xiang
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/3250
https://ink.library.smu.edu.sg/context/sis_research/article/4252/viewcontent/Online_probabilistic_learning_for_fuzzy_inference_pv.pdf
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spelling sg-smu-ink.sis_research-42522020-01-12T14:00:34Z Online probabilistic learning for fuzzy inference system OENTARYO, Richard Jayadi ER, Meng Joo LINN, San LI, Xiang 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. 2014-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3250 info:doi/10.1016/j.eswa.2014.01.034 https://ink.library.smu.edu.sg/context/sis_research/article/4252/viewcontent/Online_probabilistic_learning_for_fuzzy_inference_pv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Adaptive resonance theory Bayes' rule Kalman filter Neuro-fuzzy system Online learning Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Adaptive resonance theory
Bayes' rule
Kalman filter
Neuro-fuzzy system
Online learning
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Adaptive resonance theory
Bayes' rule
Kalman filter
Neuro-fuzzy system
Online learning
Databases and Information Systems
Numerical Analysis and Scientific Computing
OENTARYO, Richard Jayadi
ER, Meng Joo
LINN, San
LI, Xiang
Online probabilistic learning for fuzzy inference system
description 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.
format text
author OENTARYO, Richard Jayadi
ER, Meng Joo
LINN, San
LI, Xiang
author_facet OENTARYO, Richard Jayadi
ER, Meng Joo
LINN, San
LI, Xiang
author_sort OENTARYO, Richard Jayadi
title Online probabilistic learning for fuzzy inference system
title_short Online probabilistic learning for fuzzy inference system
title_full Online probabilistic learning for fuzzy inference system
title_fullStr Online probabilistic learning for fuzzy inference system
title_full_unstemmed Online probabilistic learning for fuzzy inference system
title_sort online probabilistic learning for fuzzy inference system
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/3250
https://ink.library.smu.edu.sg/context/sis_research/article/4252/viewcontent/Online_probabilistic_learning_for_fuzzy_inference_pv.pdf
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