文献类型：期刊文献

英文题名：Scattered data approximation by neural networks operators

作者：Chen, Zhixiang[1];Cao, Feilong[2]

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

年份：2016

卷号：190

起止页码：237

外文期刊名：NEUROCOMPUTING

收录：SCI-EXPANDED(收录号：WOS:000374802600025)、、EI(收录号：20161402200693)、Scopus(收录号：2-s2.0-84962076663)、WOS

基金：The authors would like to express their thanks to the reviewers for their valuable comments and suggestions. This research was supported by the National Natural Science Foundation of China under Grants 61179041, 61272023, 91330118, and 11401388.

语种：英文

外文关键词：Neural networks; Approximation; Interpolation; Scattered data; Error

外文摘要：In this paper, some feed-forward neural networks (FNNs) interpolation operators based on scattered data are introduced. Further, these operators are used as approximators to approximate bivariate continuous target function. By means of the translations and dilates of logistic function, some FNNs quasi interpolation and exact interpolation operators are constructed, respectively. Using the modulus of continuity of function and the mesh norm of scattered data as measures, the corresponding approximation errors of the constructed operators are estimated. In addition, the well-known central B-splines are used to construct FNNs interpolation operators with compact support, and the corresponding approximation errors are also estimated. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.