INFORMATION-STORAGE IN HIGH-ORDER NEURAL NETWORKS WITH UNEQUAL NEURAL ACTIVITY

被引：5
|
作者：
TAI, HM [1 ]
JONG, TL [1 ]
机构：
[1] TEXAS TECH UNIV,DEPT ELECT ENGN,LUBBOCK,TX 79409
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 1990年 / 327卷 / 01期
关键词：
D O I：
10.1016/0016-0032(90)90061-M
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Neural networks with high-order interactions only have been shown to be sufficient to provide satisfactory attractivity to the stored patterns and error corrections. Such interactions increase the storage capacity of the networks and allow one to solve a class of problems which are intractable with standard networks. In this paper we analyse the capacity of these higher-order networks by the statistical method and show why the probability of the states of neurons being active and passive can always be chosen equal, i.e. with a probability of 0.5 each. © 1990.
引用
收藏
页码：129 / 141
页数：13
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