Loading [a11y]/accessibility-menu.js
Optimal Rule-Based Granular Systems From Data Streams | IEEE Journals & Magazine | IEEE Xplore

Optimal Rule-Based Granular Systems From Data Streams


Abstract:

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 mult...Show More

Abstract:

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.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 28, Issue: 3, March 2020)
Page(s): 583 - 596
Date of Publication: 14 April 2019

ISSN Information:

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.