Approximation of fuzzy functions by regular fuzzy neural networks

被引：25
|
作者：
Huang, Huan [1 ]
Wu, Congxin [2 ]
机构：
[1] Jimei Univ, Dept Math, Xiamen 361021, Peoples R China
[2] Harbin Inst Technol, Dept Math, Harbin 150001, Peoples R China
来源：
FUZZY SETS AND SYSTEMS | 2011年 / 177卷 / 01期
关键词：
Regular fuzzy neural networks; Zadeh's extension principle; Approximation; Fuzzy functions; Fuzzy numbers; Supremum metric; Level convergence; LEVEL CONVERGENCE; ZADEHS EXTENSIONS; VALUED FUNCTIONS; CAPABILITIES;
D O I：
10.1016/j.fss.2011.04.013
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we investigate the ability of regular fuzzy neural networks to provide approximations to fuzzy functions. Since the operation of regular fuzzy neural networks is based on Zadeh's extension principle, we first present a level characterization of the Zadeh's extensions of level-continuous fuzzy-valued functions and consider the continuity of these extensions. On the basis of this, we give characterizations of fuzzy functions which can be approximated by a class of four-layer regular fuzzy neural networks according to supremum-metric and level convergence. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：60 / 79
页数：20
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