前向网络隐空间分类超平面的构造

本文讨论单隐层前向网络输出神经元所对应的隐空间分类超平面的构造问题，它直接对应于网络隐层至输出层的连接仪的设计，研究结果表明，网络隐层至支的连接权可均取为＋１，输出神经元的阈值也仅可取为１／２加上几个有限的整数中的一个，从而可以以通过对网络输入层节点至隐节点的连接权的训练，获得有效解决两类问题的前向网络。

Construction of Classfication Hyperplanes in Hidden Space for Feedforward Neural Networks

This paper discusses the problem on construction of classification hyperplane in hidden space for feedforward neural networks.This problem directly corresponds to the design of the connection weights from hidden to output neurons.The research result shows that the connection weights from hidden neurons to the output neuron can all be +1,and the bias of the output neuron can take only a basic bias of 12 plus an integer in a limited integer range as an auxiliary bias.This results that the hidden to output connection weights and bias do not need to train,which decreases the searching space of network parameters during the training process of the networks,and it is much easier for the hardware realization of the network as there is no need for multiplicator,and the network constructed in this way is somewhat robust to the input data.