Particle Swarm Optimized Autonomous Learning Fuzzy System

Xiaowei Gu, Qiang Shen, Plamen Parvanov Angelov

Research output: Contribution to journalArticlepeer-review

17 Citations (SciVal)
93 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)5352-5363
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number11
Early online date20 Feb 2020
Publication statusPublished - 01 Nov 2021


  • Autonomous learning
  • evolving intelligent system (EIS)
  • optimality
  • particle swarm optimization (PSO)


Dive into the research topics of 'Particle Swarm Optimized Autonomous Learning Fuzzy System'. Together they form a unique fingerprint.

Cite this