Bivariate fully fuzzy interpolation problem using artificial neural networks approach

作     者：Hosseini, E. Jafarian, A. 

作者机构：Islamic Azad Univ Dept Math Urmia Branch Orumiyeh Iran 

出 版 物：《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)

年 卷 期：2016年第30卷第4期

页      面：2267-2275页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bivariate fully fuzzy polynomial artificial neural network approximate solution criterion function learning algorithm 

摘      要：Artificial neural networks modeling is one of the most prominent techniques for solving more complicated mathematical problems that can not be solved in the traditional computing environments. The work described here intends to offer an efficient bivariate fuzzy interpolation methodology based on the artificial neural networks approach. It has several notable features including high processing speeds and the ability to learn the solution to a problem from a set of examples which categorizes them in line of intelligent systems. To do this, a multilayer feed-forward neural architecture is depicted for constructing a fully fuzzy interpolating polynomial of arbitrary degree. Then, a back-propagation supervised learning optimization algorithm will be applied for estimating the unknown fuzzy coefficients of the solution polynomial. Finally, the advantage of our technique is illustrated by using some practical examples to show the ability of the improved algorithm in solving rigorous problems.