New efficient neural networks for fast record detection in databases

Authors:
Hazem M. El-Bakry;Nikos Mastorakis
Affiliations:
Faculty of Computer Science & Information Systems, Mansoura University, Egypt;WSEAS Research Department, Zografou, Athens, Greece and Technical University of Sofia, Industrial Engineering Department, Sofia, Sofia, Bulgaria and Military Institutions of University Education, ...
Venue:
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2009

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Abstract

This paper presents a new approach to speed up the operation of time delay neural networks for detecting a record in databases. 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.