有界函数
乙状窦函数
人工神经网络
类型(生物学)
计算机科学
功能(生物学)
三角形不等式
算法
操作员(生物学)
度量空间
公制(单位)
构造(python库)
数学
人工智能
离散数学
数学分析
转录因子
生物
基因
抑制因子
经济
进化生物学
生物化学
化学
程序设计语言
运营管理
生态学
作者
Shaobo Lin,Jinshan Zeng,Lin Xu,Zongben Xu
标识
DOI:10.1016/j.neunet.2014.11.002
摘要
Recently, the spherical data processing has emerged in many applications and attracted a lot of attention. Among all the methods for dealing with the spherical data, the spherical neural networks (SNNs) method has been recognized as a very efficient tool due to SNNs possess both good approximation capability and spacial localization property. For better localized approximant, weighted approximation should be considered since different areas of the sphere may play different roles in the approximation process. In this paper, using the minimal Riesz energy points and the spherical cap average operator, we first construct a class of well-localized SNNs with a bounded sigmoidal activation function, and then study their approximation capabilities. More specifically, we establish a Jackson-type error estimate for the weighted SNNs approximation in the metric of L(p) space for the well developed doubling weights.
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