| Autor: |
Somaye Yeylaghi, Mahmood Otadi, Niloofar Imankhan |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
Beni-Suef University Journal of Basic and Applied Sciences, Vol 6, Iss 2, Pp 106-111 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2314-8535 |
| DOI: |
10.1016/j.bjbas.2017.01.004 |
| Popis: |
In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN) can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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