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Autonomous learning for fuzzy systems: a review.
- Source :
- Artificial Intelligence Review; Aug2023, Vol. 56 Issue 8, p7549-7595, 47p
- Publication Year :
- 2023
-
Abstract
- As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02692821
- Volume :
- 56
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Artificial Intelligence Review
- Publication Type :
- Academic Journal
- Accession number :
- 164580066
- Full Text :
- https://doi.org/10.1007/s10462-022-10355-6