期刊名称：Beni-Suef University Journal of Basic and Applied Sciences
印刷版ISSN：2314-8535
电子版ISSN：2314-8543
出版年度：2017
卷号：6
期号：2
页码：106-111
DOI：10.1016/j.bjbas.2017.01.004
语种：English
出版社：Elsevier
摘要：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.
