Fast Code Detection Using High Speed Time Delay Neural Networks

Authors:
Hazem M. El-Bakry;Nikos Mastorakis
Affiliations:
Faculty of Computer Science & Information Systems, Mansoura University, Egypt;Department of Computer Science, Military Institutions of University Education (MIUE)-Hellenic Naval Academy, Greece
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

This paper presents a new approach to speed up the operation of time delay neural networks for fast code detection. 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.