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Local optimality of self-organising neuro-fuzzy inference systems

Gu, Xiaowei, Angelov, Plamen, Rong, Hai-Jun (2019) Local optimality of self-organising neuro-fuzzy inference systems. Information Sciences, 503 . pp. 351-380. ISSN 0020-0255. (doi:10.1016/j.ins.2019.07.006) (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:90192)

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.2019.07.006

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.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2019.07.006
Uncontrolled keywords: Local optimality; Neuro-fuzzy system; Evolving intelligent system; Self-organising; Data partitioning
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 08:12 UTC
Last Modified: 15 Sep 2021 16:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90192 (The current URI for this page, for reference purposes)
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