Improvement of Associative Memory for One to Many with a Quantized Storage Algorithm

被引：0
作者：
Qi, Yi [1 ]
机构：
[1] Chong Qing Univ Arts & Sci, Sch Comp, Chongqing, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON MODELING, SIMULATION AND OPTIMIZATION, PROCEEDINGS | 2009年
关键词：
neural networks; associative memory; quantized storage; distributed; omputing;
D O I：
10.1109/APCIP.2009.54
中图分类号：
S2 [农业工程];
学科分类号：
0828 ;
摘要：
The severe storage restrictions of existing classical associative memory is a great obstacle to obtain dynamic knowledge increase of hetero associative memory. And quantum neural networks may be one of next directions in the evolution of neural distributed computing system. In this paper, we proposed an new multi module associative memory model with a quantized storage algorithm and try to improve the performance of new memory. The new method may expand the storage capacity and achieve an exponential growth in base two using only n neurons. The properties of new associative memory are investigated in detail.
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收藏
页码：187 / 190
页数：4
相关论文
共 9 条
  • [1] [Anonymous], J COMPUTER RES DEV
  • [2] Associative memory for intelligent control
    Hattori, M
    Hagiwara, M
    [J]. MATHEMATICS AND COMPUTERS IN SIMULATION, 2000, 51 (3-4) : 349 - 374
  • [3] ON QUANTUM NEURAL COMPUTING
    KAK, S
    [J]. INFORMATION SCIENCES, 1995, 83 (3-4) : 143 - 160
  • [4] OHSUMI T, 1992, IEICE JAPAN D, V75, P1948
  • [5] Osana Y, 1998, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, P514, DOI 10.1109/IJCNN.1998.682320
  • [6] Perus M., 2000, Neural Network World, V10, P1001
  • [7] SANJAY G, 2001, J COMPUTER SYSTEM SC, V63, P355
  • [8] VENTURA D, 1998, NEUR NETW P 1998 IEE, V1, P509
  • [9] YI Q, 2009, INF COMP SCI 2009 IC, V2, P181
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