Particle Swarm Optimized Autonomous Learning Fuzzy System
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 300843644
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2020.2967462
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 0
- Volume
- 0
- Issue
- -
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
B - Data Science
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The Autonomous learning multiple model (ALMMo) systems are critically important for dynamically evolving, non-stationary processes and systems modelling, prediction and control. Their design, however, is more difficult than that of static systems with constant structure. Traditionally, all the efforts in the design are directed towards the paprametric consequent part minimising the error obtaining the “best” parameter values. However, this optimality is subject to a certain antecedent structure of the system. In this paper, for the first time the antecedent part is being optimised by particle swarm optimisation method. This results in significant improvement of the performance.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
