Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7211491 | Beni-Suef University Journal of Basic and Applied Sciences | 2017 | 19 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Engineering
Engineering (General)
Authors
Somaye Yeylaghi, Mahmood Otadi, Niloofar Imankhan,