基于神经网络的手写体数字识别

本文将奇异值分解方法用于神经网络结构优化。通过收敛后网络的权值矩阵作ＳＶＤ分解，根据分解结果确定出较适宜的隐层神经元数目，这样神经网络结构得以简单化，连接权的数目将减少，从而使计算理减少，节约存贮资源及时间。

Recognition of the handwriting Numbers Bases on Neural Network

This paper adopts the way of Singular Value Decomposition(SVD)to sim-plify the neural network structure. The right number of hidden unit can be choosen by us-ing the result of decomposing the weight matrix after the network is converged.Thus the weight numbers will be reduced and many advantages will be obtained while training a network.Also this paper puts forward a way about locating the characteristic points and uses attributions of these points and their relative position as neural network's input.The result of experiment has shown that these characteristics have been well reflected the char-acter structure information.