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An adaptive learning algorithm for a wavelet neural network
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DOI：10.1111/j.1468-0394.2005.00314.x.png)
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摘要
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An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals.
Keyword:
prediction
emulation
non-stationary noisy signals
hybrid wavelet neural network
online learning algorithm
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