Open AccessA new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management
Author(s) -
Somaye Yeylaghi,
Mahmood Otadi,
Niloofar Imankhan
Publication year - 2017
Publication title -
beni-seuf university journal of basic and applied sciences /beni-suef university journal of basic and applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2314-8543
pISSN - 2314-8535
DOI - 10.1016/j.bjbas.2017.01.004
Subject(s) - fuzzy logic , neuro fuzzy , interval (graph theory) , artificial neural network , computer science , adaptive neuro fuzzy inference system , fuzzy set operations , soft computing , fuzzy number , defuzzification , field (mathematics) , artificial intelligence , fuzzy classification , data mining , fuzzy set , mathematical optimization , mathematics , fuzzy control system , combinatorics , pure mathematics
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