[1]王传栋 杨雁莹[].自联想记忆神经网络研究[J].计算机技术与发展,2011,(03):109-112.
 WANG Chuan-dong,YANG Yan-ying.Research on Auto-Associative Memory Neural Networks[J].,2011,(03):109-112.
点击复制

自联想记忆神经网络研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年03期
页码:
109-112
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Research on Auto-Associative Memory Neural Networks
文章编号:
1673-629X(2011)03-0109-04
作者:
王传栋12 杨雁莹[3]
[1]南京邮电大学计算机学院[2]南京邮电大学计算机技术研究所[3]南京森林警察学院信息技术系
Author(s):
WANG Chuan-dong YANG Yan-ying
[1]College of Computer, Nanjing University of Posts & Telecommunications[2]Institute of Computer Technology, Nanjing University of Posts & Telecommunications[3]Department of Information and Technology, Nanjing College of Forestry Police
关键词:
神经网络自联想记忆智能信息处理
Keywords:
neural network auto-associative memory intelligent information processing
分类号:
TP183
文献标志码:
A
摘要:
自联想记忆神经网络能模拟人脑思维和机器智能,具有信息分布式存储和内容可寻址访问的重要特征,是人工神经网络研究的一个重要分支。介绍了开创自联想记忆神经网络研究先河的Hopfield联想记忆神经网络模型,分析了该模型的优缺点;然后在系统分析现有白联想记忆神经网络相关研究文献的基础上,从学习算法、体系结构和应用领域三个方面对自联想记忆神经网络的研究进展进行了归纳阐述;总结了自联想记忆神经网络目前存在的主要问题,并且预测了其未来的发展趋势
Abstract:
As an important artificial neural network, auto-associative memory model (AM) can be employed to mimic human thinking and machine intelligence, which has massively parallel distributed configuration and content-addressable ability. In this paper, introduce in detail the Hopfield Associative Memory (HAM) neural network which has yielded a great impact on the development of auto-associative memory model, and analyze HAM' s strongpoint and drawback. Secondly, focusing on the existing relevant research literatures, present a survey of auto-associative memory models from the three aspects such as learning algorithm, network architecture and practical application; Finally,summarize the main question which auto-associative memory models are faced with at present, and forecast its future development tendency

相似文献/References:

[1]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
 LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(03):84.
[2]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
 GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(03):148.
[3]包力伟 周俊.铸锻企业生产质量控制系统的开发[J].计算机技术与发展,2008,(04):174.
 BAO Li-wei,ZHOU Jun.Development of a Manufacture Quality Control System in Casting Company[J].,2008,(03):174.
[4]李志俊 程家兴 金奎 饶玉佳.基于样本期望训练数的BP神经网络改进研究[J].计算机技术与发展,2009,(05):103.
 LI Zhi-jun,CHENG Jia-xing,JIN Kui,et al.BP Algorithm Improvement Based on Sample Expected Training Number[J].,2009,(03):103.
[5]李龙澍 葛瑞峰 王慧萍.基于神经网络的批强化学习在Robocup中的应用[J].计算机技术与发展,2009,(07):98.
 LI Long-shu,GE Rui-feng,WANG Hui-ping.Application of Batch Reinforcement Learning Based on NN to Robocup[J].,2009,(03):98.
[6]贾志先.神经网络在空白试卷识别中的应用[J].计算机技术与发展,2009,(08):208.
 JIA Zhi-xian.Application of Neural Network in Recognization Blank Examination Paper[J].,2009,(03):208.
[7]肖宜龙 路游 亓永刚.基于神经网络的NURBS曲面重建[J].计算机技术与发展,2009,(09):65.
 XIAO Yi-long,LU You,QI Yong-gang.NURBS Surface Reconstruction Based on Neural Network[J].,2009,(03):65.
[8]蔡秋茹 罗烨 柳益君 叶飞跃.企业资信的BP神经网络评估模型研究[J].计算机技术与发展,2009,(10):117.
 CAI Qiu-ru,LUO Ye,LIU Yi-jun,et al.Research on BP Neural Network Model for Corporation Credit Rating[J].,2009,(03):117.
[9]王晓敏 刘希玉 戴芬.BP神经网络预测算法的改进及应用[J].计算机技术与发展,2009,(11):64.
 WANG Xiao-min,LIU Xi-yu,DAI Fen.Improvement and Application of BP Neural Network Forecasting Algorithm[J].,2009,(03):64.
[10]崔海青 刘希玉.基于粒子群算法的RBF网络参数优化算法[J].计算机技术与发展,2009,(12):117.
 CUI Hai-qing,LIU Xi-yu.Parameter Optimization Algorithm of RBF Neural Network Based on PSO Algorithm[J].,2009,(03):117.

备注/Memo

备注/Memo:
国家自然科学基金(61003040);南京邮电大学校科研基金(NY210043)王传栋(1971-),男,讲师,硕士,研究方向为神经网络与模式识别、数据仓库与数据挖掘;杨雁莹,副教授,硕士,研究方向为软件工程与数据库、数据挖掘
更新日期/Last Update: 1900-01-01