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Self-boosting first-order autonomous learning neuro-fuzzy systems

Gu, Xiaowei, Angelov, Plamen (2019) Self-boosting first-order autonomous learning neuro-fuzzy systems. Applied Soft Computing, 77 . pp. 118-134. ISSN 1568-4946. (doi:10.1016/j.asoc.2019.01.005) (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:90200)

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.asoc.2019.01.005

Abstract

In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2019.01.005
Uncontrolled keywords: Neuro-fuzzy systems; Autonomous learning; Local optimality; Self-boosting; Streaming data processing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 12:36 UTC
Last Modified: 15 Sep 2021 16:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90200 (The current URI for this page, for reference purposes)
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