A Structural Evolving Approach for Fuzzy Systems
| Autor: | Hisham Haider Yusef Sa'ad, Nor Ashidi Mat Isa, Md. Manjur Ahmed |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fuzzy rule
Computer science Applied Mathematics Linear system 02 engineering and technology Fuzzy control system computer.software_genre Fuzzy logic Partition (database) Low complexity Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering Adaptive system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer Interpretability |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 28:273-287 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2019.2904928 |
| Popis: | A structural evolving approach (SEA) based on incremental partitioning learning is proposed in this paper. SEA starts with a simple fuzzy system with one fuzzy rule containing no fuzzy term for the antecedent part. After that, it keeps evolving by adding new fuzzy terms to the selected attribute in the selected partition. A new partitioning technique that locates sufficient splitting points is proposed. Furthermore, a dynamic partition-selection technique that leads to the best tradeoff between accuracy and interpretability is presented. In addition, a rule reduction mechanism that is able to locate rules with low and high impact on the system is developed. Finally, a local linear optimization is used to find the consequent parameters, which means that SEA uses only linear systems to find the antecedents and consequents parameters. Therefore, a simple and high interpretability system is offered. Six data sets are used to validate the performance of SEA with similar works. Despite the low complexity of SEA, the results show that SEA outperforms the existing methods with fewer fuzzy rules and fewer antecedent conditions too. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|