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Particle Swarm Optimized Autonomous Learning Fuzzy System

Gu, Xiaowei, Shen, Qiang, Angelov, Plamen P. (2020) Particle Swarm Optimized Autonomous Learning Fuzzy System. IEEE Transactions on Cybernetics, . pp. 1-12. ISSN 2168-2267. E-ISSN 2168-2275. (doi:10.1109/TCYB.2020.2967462) (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:90179)

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/10.1109/TCYB.2020.2967462

Abstract

The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the ``one pass'' learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

Item Type: Article
DOI/Identification number: 10.1109/TCYB.2020.2967462
Uncontrolled keywords: Optimization; Silicon; Fuzzy systems; Particle swarm optimization; Intelligent systems; Search problems; Prediction algorithms; Autonomous learning; evolving intelligent system (EIS); optimality; particle swarm optimization (PSO)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
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: 13 Sep 2021 11:34 UTC
Last Modified: 14 Sep 2021 09:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90179 (The current URI for this page, for reference purposes)
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