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基于Hopfield神经网络噪声数字的识别
引用本文:傅德胜,张学勇.基于Hopfield神经网络噪声数字的识别[J].通信技术,2010,43(1):126-128,187.
作者姓名:傅德胜  张学勇
作者单位:南京信息工程大学计算机与软件学院,江苏,南京,210044
摘    要:噪声数字的识别具有很好的应用前景,也是后期处理的基础。基于离散Hopfield神经网络的联想记忆能力,通过改进神经网络的记忆样本,再利用Hebb规则对改进的记忆样本进行学习,得到权值矩阵,根据待识别的噪声数字的信息联想起记忆的数字。利用改进后的离散Hopfield神经网络对噪声数字进行了识别的实验。实验结果表明,该方法提高了传统网络的记忆能力和识别的正确率。

关 键 词:噪声数字  离散Hopfield  正交化

Noise-Figure Identification Based on Hopfield Neural Network
FU De-sheng,ZHANG Xue-yong.Noise-Figure Identification Based on Hopfield Neural Network[J].Communications Technology,2010,43(1):126-128,187.
Authors:FU De-sheng  ZHANG Xue-yong
Affiliation:FU De-sheng,ZHANG Xue-yong(Computer , Software College,Nanjing University of Information Science & Technology,Nanjing Jiangsu 210044,China)
Abstract:Identification of the noise figure has a good application prospect and is also the basis for post-processing.Based on the associative memory ability of discrete Hopfield neural network,and by improving the memory sample and using Hebb rule to learn the improved memory sample,the weight value matrix is obtained,the noise figure would be identified according to the information of noise fingure.The identification experiment on noise figure by using the improved Hopfield neural network shows that the method imp...
Keywords:noise figure  discrete Hopfield  orthogonalization  
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