Modified time delay neural networks for fast data processing
Hazem Mokhtar El-Bakry, Qiangfu Zhao · 2006
In recent years, time delay neural networks are successfully used in many applications. Here, a new idea to speed the operation of time delay neural networks is presented. The whole data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks uses cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically that the number of computation steps required for the presented fast time delay neural networks is less than that needed by classical time delay neural networks. Simulation results after these corrections using MATLAB confirms the theoretical computations.