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| AN ADAPTIVE TIME DELAY WAVELET NEURAL NETWORK FOR SIGNAL APPROXIMATION |
| He Zhenya; Li Wenhua; Wei Chengjian |
| Southeast University Department of Radio Engineering Nanjing 210096 |
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Abstract Wavelet neural networks (WNN) is a powerful tool for function approximation. In this paper a new model named adaptive time delay WNN(ATDWNN) is proposed which combines time delay neural network and wavelet decomposition. ATDWNN is used to approximate signals having different time delays in the same class. In order to train ATDWNN, time mechanism based competition learning is also proposed. It is shown through experiments that ATDWNN can not only approximate signals having different time delays by the same superwavelet, but also detect these time delays successfully.
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Received: 02 July 1996
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1998, Vol. 20 
