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A self-adaptive fuzzy learning system for streaming data prediction

Gu, Xiaowei, Shen, Qiang (2021) A self-adaptive fuzzy learning system for streaming data prediction. Information Sciences, 579 . pp. 623-647. ISSN 0020-0255. (doi:10.1016/j.ins.2021.08.023) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90398)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.ins.2021.08.023

Abstract

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from data streams a predictive model composed of a set of prototype-based fuzzy rules, with each of which representing a certain local data distribution, and continuously self-evolves to follow the changing data patterns in non-stationary environments. Unlike conventional evolving fuzzy systems, both the fuzzy inference and consequent parameter learning schemes utilised by SAFL are simplified so that only a small number of selected fuzzy rules within the rule base are involved in system output generation and parameter updating during a learning cycle. Such simplification not only significantly reduces the system’s computational complexity but also increases its prediction precision. In addition, both theoretical and empirical investigations guarantee the stability of the resulting SAFL. Comparative experimental studies on a wide variety of benchmark and real-world problems demonstrate that SAFL is able to learn from streaming data in a highly efficient manner and to make predictions with a great accuracy, revealing the effectiveness and validity of the proposed approach.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2021.08.023
Uncontrolled keywords: Evolving fuzzy system; Fuzzy inference; Streaming data; Stability
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 09:40 UTC
Last Modified: 29 Sep 2021 11:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90398 (The current URI for this page, for reference purposes)
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