A new state space model for a complex RTRL neural network

被引：0
|
作者：
Coelho, PHGA [1 ]
机构：
[1] Univ Estado Rio De Janeiro, Elect & Telecommun Dept, BR-20559900 Rio De Janeiro, RJ, Brazil
来源：
IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2001年
关键词：
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：
The purpose of this work is to represent the complex RTRL fully recurrent neural network in a state space model for engineering applications such as mobile channel equalization. This representation extends Haykin's [1]for complex valued inputs, yielding a compact formulation useful in possible changes in the training of a fully recurrent neural network. Numerical results are presented to illustrate the method.
引用
收藏
页码：1756 / 1761
页数：6
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