Home > Research > Publications & Outputs > Local Optimality of Self-Organising Neuro-Fuzzy...

Electronic data

  • OptimalSOF_minorrevision_v7

    Rights statement: This is the author’s version of a work that was accepted for publication in Information Sciences. 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 Information Sciences, 503, 2019 DOI: 10.1016/j.ins.2019.07.006

    Accepted author manuscript, 1.52 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Text available via DOI:

View graph of relations

Local Optimality of Self-Organising Neuro-Fuzzy Inference Systems

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>30/11/2019
<mark>Journal</mark>Information Sciences
Volume503
Number of pages30
Pages (from-to)351-380
<mark>State</mark>Published
Early online date3/07/19
<mark>Original language</mark>English

Abstract

Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-organising neuro-fuzzy inference system (SONFIS) as an example of typical zero-order EISs and analyse the local optimality of its solutions. The optimality problem is firstly formulated in a mathematical form, and detailed optimality analysis is conducted. The conclusion is that SONFIS does not generate a locally optimal solution in its original form. Then, an optimisation method is proposed for SONFIS, which helps the system to attain local optimality in a few iterations using historical data. Numerical examples presented in this paper demonstrate the validity of the optimality analysis and the effectiveness of the proposed optimisation method. In addition, it is further verified numerically that the proposed concept and general principles can be applied to other types of zero-order EISs with similar operating mechanisms.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Information Sciences. 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 Information Sciences, 503, 2019 DOI: 10.1016/j.ins.2019.07.006