Optimal Rule-Based Granular Systems From Data Streams

We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular cove...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2020-03, Vol.28 (3), p.583-596
Main Authors: Leite, Daniel, Andonovski, Goran, Skrjanc, Igor, Gomide, Fernando
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
Adaptive systems
Computational modeling
Data models
Data transmission
evolving systems
granular computing
information specificity
Level set
Mathematical model
Multiple objective analysis
Nonstationary environments
Numerical models
online data stream
Optimization
Predictive control
Predictive models
System identification
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use α-level sets over Gaussian membership functions to set model granularity and operate with hyperrectangular forms of granules in nonstationary environments. The resulting rule-based systems are formed in a formal and systematic fashion. They can be useful in time series modeling, dynamic system identification, predictive analytics, and adaptive control. Precise estimates and enclosures are given by linear piecewise and inclusion functions related to optimal granular mappings.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2911493