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

Gu, Xiaowei and Angelov, Plamen Parvanov (2019) Self-boosting first-order autonomous learning neuro-fuzzy systems. Applied Soft Computing, 77. pp. 118-134. ISSN 1568-4946

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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:
Journal Article
Journal or Publication Title:
Applied Soft Computing
Additional Information:
This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 77, 2019 DOI: 10.1016/j.asoc.2019.01.005
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Deposited On:
04 Jan 2019 15:55
Last Modified:
11 May 2022 05:47