Probability density estimation using adaptive activation function neurons

被引：16
|
作者：
Fiori, S
Bucciarelli, P
机构：
[1] Univ Perugia, Neural Networks & Adapt Syst Res Grp, Dept Ind Engn, Perugia, Italy
[2] Univ Ulm, Zent Inst Biomed Tech, Ulm, Germany
来源：
NEURAL PROCESSING LETTERS | 2001年 / 13卷 / 01期
关键词：
adaptive activation function neurons; cumulative distribution function; differential entropy; probability density function; stochastic gradient;
D O I：
10.1023/A:1009635129159
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we deal with the problem of approximating the probability density function of a signal by means of adaptive activation function neurons. We compare the proposed approach to the one based on a mixture of kernels and show through computer simulations that comparable results may be obtained with limited expense in computational efforts.
引用
收藏
页码：31 / 42
页数：12
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