An Introduction to Complex-Valued Recurrent Correlation Neural Networks

被引：0
|
作者：
Valle, Marcos Eduardo [1 ]
机构：
[1] Univ Estadual Campinas, Dept Appl Math, Campinas, SP, Brazil
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
CORRELATION ASSOCIATIVE MEMORIES; OPTIMIZATION; RECALL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we generalize the bipolar recurrent correlation neural networks (RCNNs) of Chiueh and Goodman for complex-valued vectors. A complex-valued RCNN (CV-RCNN) is characterized by a possible non-linear function which is applied on the real part of the scalar product of the current state and the fundamental vectors. Computational experiments reveal that some CV-RCNNs can implement associative memories with high-storage capacity. Furthermore, these CV-RCNNs exhibit an excellent noise tolerance.
引用
收藏
页码：3387 / 3394
页数：8
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