APPROXIMATION ANALYSES FOR FUZZY VALUED FUNCTIONS IN L_1(μ)-NORM BY REGULAR FUZZY NEURAL NETWORKS  

作　　者：Liu Puyin (Dept. of System Eng. and Math., National Univ. of Defence Tech., Changsha 410073) 

出　　处：《Journal of Electronics(China)》2000年第2期132-138,共7页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No:69872039)

摘　　要：By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.

关 键 词：FUZZY VALUED simple function REGULAR FUZZY neural network L1(μ) APPROXIMATION Universal approximator 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]