文献类型：期刊文献

中文题名：The construction and approximation of some neural networks operators

英文题名：The construction and approximation of some neural networks operators

作者：Chen Zhi-xiang[2];Cao Fei-long[1];Zhao Jian-wei[1]

机构：[1]China Jiliang Univ, Dept Math, Hangzhou 310018, Zhejiang, Peoples R China;[2]Shaoxing Univ, Dept Math, Shaoxing 312000, Peoples R China

年份：2012

卷号：27

期号：1

起止页码：69

中文期刊名：高校应用数学学报：英文版（B辑）

外文期刊名：APPLIED MATHEMATICS-A JOURNAL OF CHINESE UNIVERSITIES SERIES B

收录：SCI-EXPANDED(收录号：WOS:000301606000006)、、Scopus(收录号：2-s2.0-84863375184)、CSCD2011_2012、WOS、CSCD

基金：Supported by the National Natural Science Foundation of China (61179041, 61101240) and the Zhejiang Provincial Natural Science Foundation of China (Y6110117).

语种：英文

中文关键词：网络运营商;级数逼近;神经;Riesz平均;度量函数;时间间隔;连续函数;二元函数

外文关键词：approximation; sigmoidal function; neural network operator; Bochner-Riesz mean

中文摘要：In this paper, the technique of approximate partition of unity is used to construct a class of neural networks operators with sigmoidal functions. Using the modulus of continuity of function as a metric, the errors of the operators approximating continuous functions defined on a compact interval are estimated. Furthmore, Bochner-Riesz means operators of double Fourier series are used to construct networks operators for approximating bivariate functions, and the errors of approximation by the operators are estimated.

外文摘要：In this paper, the technique of approximate partition of unity is used to construct a class of neural networks operators with sigmoidal functions. Using the modulus of continuity of function as a metric, the errors of the operators approximating continuous functions defined on a compact interval are estimated. Furthmore, Bochner-Riesz means operators of double Fourier series are used to construct networks operators for approximating bivariate functions, and the errors of approximation by the operators are estimated.