文件名称:Novel-Neuronal-Activation
-
所属分类:
- 标签属性:
- 上传时间:2017-02-12
-
文件大小:462.64kb
-
已下载:0次
-
提 供 者:
-
相关连接:无下载说明:别用迅雷下载,失败请重下,重下不扣分!
介绍说明--下载内容来自于网络,使用问题请自行百度
Feedforward neural network structures have extensively been considered in the
literature. In a significant volume of research and development studies hyperbolic tangent
type of a neuronal nonlinearity has been utilized. This paper dwells on the widely used neuronal
activation functions as well as two new ones composed of sines and cosines, and a sinc
function characterizing the firing of a neuron. The viewpoint here is to consider the hidden
layer(s) as transforming blocks composed of nonlinear basis functions, which may assume
different forms. This paper considers 8 different activation functions which are differentiable
and utilizes Levenberg-Marquardt algorithm for parameter tuning purposes. The studies
carried out have a guiding quality based on empirical results on several training data sets.
literature. In a significant volume of research and development studies hyperbolic tangent
type of a neuronal nonlinearity has been utilized. This paper dwells on the widely used neuronal
activation functions as well as two new ones composed of sines and cosines, and a sinc
function characterizing the firing of a neuron. The viewpoint here is to consider the hidden
layer(s) as transforming blocks composed of nonlinear basis functions, which may assume
different forms. This paper considers 8 different activation functions which are differentiable
and utilizes Levenberg-Marquardt algorithm for parameter tuning purposes. The studies
carried out have a guiding quality based on empirical results on several training data sets.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
Novel Neuronal Activation.pdf
本网站为编程资源及源代码搜集、介绍的搜索网站,版权归原作者所有! 粤ICP备11031372号
1999-2046 搜珍网 All Rights Reserved.