This paper addresses the mapping capacity of feedforward neural networks (FFNNs). We propose a new condition of the activation function for FFNNs to be capable of approximating any continuous maps defined on /spl Rfr//sup n/. Based on the result, we construct several FFNN based nonlinear ...
Accelerating the Training of Feedforward Neural Networks Using Improving Unsupervised Learning Principle Accelerating the training of feedforward neural networks using generalized Hebbian rules for initializing the internal r epresentations[J].IEEE Transactions on ... BA Fang,MX Zhu 被引量: 5发表: 2000...
Xavier——Understanding the difficulty of training deep feedforward neural networks 1. 摘要 本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法。 作者发现 sigmoid 函数不适合深度网络,在这种情况下,随机初始化参数会让较深的隐藏层陷入到饱和区域。 作者提...
初始化的梯度分析: 文中基于Bradley(2009)的理论分析 the variance of the back-propagated gradients,并提出一种新的权值初始化的方法。 分析的前提:1. 网络在初始化处于线性条件下,即激活活函数的导数为1;2. 初始化的权值的mean 为0,且独立同分布的;3, 输入特征 x 的 variance是相同的。经过一系列推导,得...
论文解析-《Understanding the difficulty of training deep feedforward neural networks》 这篇论文详细解析了深度网络中参数xavier初始化方法,这里做一下读书笔记,同时记录一下自己的理解。 1 引言 经典前馈神经网络其实很早就有了(Remelhart et al.,1986),近年来对深度监督神经网络的一些成果只不过在初始化和训练...
On the number of training points needed for adequate training of feedforward neural networks The authors address the problem of training neural networks to act as approximations of continuous mappings. In the case where the only representation of t... KS Hashemi,RJ Thomas - International Forum on...
Xavier——Understanding the difficulty of training deep feedforward neural networks 1. 摘要 本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法。 作者发现 sigmoid 函数不适合深度网络,在这种情况下,随机初始化参数会让较深的隐藏层陷入到饱和区域。
Xavier——Understanding the difficulty of training deep feedforward neural networks,1.摘要本文尝试解释为什么在深度的神经网络中随机初始化会让梯度下降表现很差,并且在此基础上来帮助设计更好的算法。作者发现sigmoid函数不适合深度网络,在这种情况下,随机初始化
Thibault, J.: Feedforward neural networks for the identifica- tion of dynamic processes. Chem. Eng. Commun., 105, 109128 (1991).Thibault, J., Feedforward neural networks for identification of dynamic processes, J. Chem. Eng. Comm 105, 109-128, 1991....
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