Complex-Valued Feedforward Neural Networks Learning Without Backpropagation

被引：1
|
作者：
Guo, Wei [1 ]
Huang, He [1 ]
Huang, Tingwen [2 ]
机构：
[1] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China
[2] Texas A&M Univ Qatar, Doha 5825, Qatar
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV | 2017年 / 10637卷
基金：
中国国家自然科学基金;
关键词：
Complex-valued neural networks; Forward-only computation; Levenberg-Marquardt algorithm; Computation cost; Classification; SYSTEMS;
D O I：
10.1007/978-3-319-70093-9_11
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an efficient learning algorithm for complex-valued feedforward neural networks with application to classification problems. It simplifies complex-valued neural networks learning by using the forward-only computation rather than traditional forward and backward computations. By incorporating the forward-only computation, the complex-valued Levenberg-Marquardt algorithm becomes more efficient. Comparison results of computation cost show that the proposed forward-only complex-valued learning algorithm can be faster than the traditional implementation of the Levenberg-Marquardt algorithm.
引用
收藏
页码：100 / 107
页数：8
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