Szczegóły publikacji
Opis bibliograficzny
A new particle swarm optimisation based memetic procedure for fuzzy J-K flop neural networks learning / Piotr A. KOWALSKI, Tomasz Słoczyński // W: FUZZ-IEEE 2023 [Dokument elektroniczny] : 2023 IEEE International conference on Fuzzy systems (FUZZ) : [13–17 August 2023, Incheon, Republic of Korea] : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2023. — (IEEE International Fuzzy Systems Conference Proceedings ; ISSN 1544-5615). — e-ISBN: 979-8-3503-3228-5. — S. [1–6]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [6], Abstr. — Publikacja dostępna online od: 2023-11-09. — P. A. Kowalski - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151366 |
|---|---|
| Data dodania do BaDAP | 2024-01-23 |
| Tekst źródłowy | URL |
| DOI | 10.1109/FUZZ52849.2023.10309703 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Fuzzy Systems 2023 |
| Czasopismo/seria | IEEE International Fuzzy Systems conference proceedings |
Abstract
Fuzzy Flip-Flop Neural Networks are a type of neural network that combines the strengths of fuzzy logic and recurrent neural networks. In this article, a new method of training the Fuzzy Flip-Flop Neural Network is described. This combines classic particle swarm optimisation procedure with selected evolutionary procedures. Among these are mutation or crossing. The proposed algorithm is tested both on the example of regression of a simple function and then on benchmark data used for classification. In most test cases, the memetic procedure has been shown to provide fast convergence, high accuracy, and robustness, making it a popular choice for the learning and optimisation of Fuzzy Flip-Flop Neural Networks.
