基于离散Hopfield神经网络的噪声数字识别

在Hebb学习规则的基础上，运用离散Hopficld神经网络的联想记忆能力，对含有噪声而产生畸变的0~9数字进行了识别。通过改进神经网络的记忆样本，即先对记忆样本做正交化处理，再对改进后的记忆样本进行学习，得到相应的权值矩阵，然后利用改进后的离散Hopfield神经网络根据待识别噪声数字的信息联想已记忆的数字。实验结果表明，改进后的神经网络对噪声数字有较好的识别效果，提高了记忆能力和识别的正确率。

Noise-figure Recognition Based on Discrete Hopfield Neural Network

Based on the Hebb learning rule, noised and distortion figures of 0~9 were identified, using the associative memory ability of discrete Hopfield neural network. Through improving the memory samples, which is to be orthogonal, and using Hebb rule to learn the improved memory sample, the weight value matrix was obtained, the noise figure would be identified according to the information of noise figure. The identification experiment on noise figure by using the improved Hopfield neural network shows that the method improves the memory capability and the correct identificanon rate of traditional network.