Comparison of Complex- and Real-Valued Feedforward Neural Networks in Their Generalization Ability

被引：0

作者：

Hirose, Akira ^{[1
]}

Yoshida, Shotaro ^{[1
]}

机构：

[1] Univ Tokyo, Dept Elect Engn & Informat Syst, Tokyo 1138654, Japan

来源：

NEURAL INFORMATION PROCESSING, PT I | 2011年 / 7062卷

关键词：

Complex-valued neural network; function approximation; generalization; BACKPROPAGATION ALGORITHM;

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：

We compare the generalization characteristics of complex-valued and real-valued feedforward neural networks when they deal with wave-related signals. We assume a task of function approximation. Experiments demonstrate that complex-valued neural networks show smaller generalization error than real-valued ones in particular when the signals have high degree of wave nature.

引用

页码：526 / +

页数：2

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