Fast time delay neural networks

被引：16

作者：

El-Bakry, HM ^{[1
]}

Zhao, QF ^{[1
]}

机构：

[1] Univ Aizu, Aizu Wakamatsu 9658580, Japan

来源：

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS | 2005年 / 15卷 / 06期

关键词：

fast time delay neural networks; cross correlation; frequency domain; real and complex numbers;

D O I：

10.1142/S0129065705000414

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 a new approach to speed up the operation of time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.

引用

页码：445 / 455

页数：11

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