具有直接和交叉前向连接的多层神经网络分析

S. Placzek, B. Adhikari
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引用次数: 13

摘要

由于许多实际原因,人工神经网络引起了人们的极大兴趣。时至今日,它们已被广泛实施。在许多可能的人工神经网络中,应用最广泛的是具有直接连接的反向传播模型。在这个模型中,输入层是输入数据,每一层都是前一层的输出。可以通过向每一层提供输入数据来扩展该模型。本文认为,这种被称为交叉前向连接(Cross - Forward Connection)的新模式比广泛使用的直接连接(Direct Connection)更优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Multilayer Neural Networks with Direct and Cross-Forward Connection
Artificial Neural Networks are of much interest for many practical reasons. As of today, they are widely implemented. Of many possible ANNs, the most widely used one is the back-propagation model with direct connection. In this model the input layer is fed with input data and each subsequent layers are fed with the output of preceding layer. This model can be extended by feeding the input data to each layer. This article argues that this new model, named Cross Forward Connection, is optimal than the widely used Direct Connection.
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