Autonomous Learning for Fuzzy Systems: A Review

Xiaowei Gu, Jungong Han, Qiang Shen, Plamen Angelov

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl Adolyguadolygiad gan gymheiriaid

6 Dyfyniadau(SciVal)
80 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)7549-7595
Nifer y tudalennau47
CyfnodolynArtificial Intelligence Review
Cyfrol56
Rhif cyhoeddi8
Dyddiad ar-lein cynnar15 Rhag 2022
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Awst 2023

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