scholarly journals Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain

2005 ◽  
Vol 2005 (13) ◽  
Author(s):  
Hazem M. El-Bakry ◽  
Qiangfu Zhao
Keyword(s):  
Neural Networks ◽  
Frequency Domain ◽  
Cross Correlation ◽  
Pattern Detection ◽  
Fast Pattern Detection

FAST TIME DELAY NEURAL NETWORKS

2005 ◽  
Vol 15 (06) ◽  
pp. 445-455 ◽  
Author(s):  
HAZEM M. EL-BAKRY ◽  
QIANGFU ZHAO
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Frequency Domain ◽  
Cross Correlation ◽  
Input Pattern ◽  
Fast Time ◽  
New Approach ◽  
Entire Data ◽  
Speed Up ◽  
Simulation Results

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.

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