Szczegóły publikacji
Opis bibliograficzny
Hybrid system of ART and RBF neural networks for online clustering / Andrzej BIELECKI, Mateusz WÓJCIK // Applied Soft Computing ; ISSN 1568-4946. — 2017 — vol. 58, s. 1–10. — Bibliogr. s. 9–10, Abstr. — Publikacja dostępna online od: 2017-04-18
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 105920 |
|---|---|
| Data dodania do BaDAP | 2017-05-29 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.asoc.2017.04.012 |
| Rok publikacji | 2017 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Applied Soft Computing |
Abstract
An online clustering task is considered for machine state monitoring purpose. In the previous authors’ researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found.
