Back to Search Start Over

Particle Swarm Optimized Autonomous Learning Fuzzy System.

Authors :
Gu, Xiaowei
Shen, Qiang
Angelov, Plamen P.
Source :
IEEE Transactions on Cybernetics; Nov2021, Vol. 51 Issue 11, p5352-5363, 12p
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682267
Volume :
51
Issue :
11
Database :
Complementary Index
Journal :
IEEE Transactions on Cybernetics
Publication Type :
Academic Journal
Accession number :
153789606
Full Text :
https://doi.org/10.1109/TCYB.2020.2967462