Fault Diagnosis Using Wavelet Neural Networks
Neural Processing Letters
Adaptive wavelet neural network for prediction of hourly NOX and NO2 concentrations
WSC '04 Proceedings of the 36th conference on Winter simulation
Nonlinear systems control using self-constructing wavelet networks
Applied Soft Computing
Feature extraction for pulmonary crackle representation via wavelet networks
Computers in Biology and Medicine
Neurocomputing
Wavelet differential neural network observer
IEEE Transactions on Neural Networks
Fault data compression of power system with wavelet neural network based on wavelet entropy
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Comparison of wavenet and neuralnet for system modeling
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Wavelet neural networks: A practical guide
Neural Networks
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A model of multiwavelet-based neural networks is proposed. Its universal and L2 approximation properties, together with its consistency are proved, and the convergence rates associated with these properties are estimated. The structure of this network is similar to that of the wavelet network, except that the orthonormal scaling functions are replaced by orthonormal multiscaling functions. The theoretical analyses show that the multiwavelet network converges more rapidly than the wavelet network, especially for smooth functions. To make a comparison between both networks, experiments are carried out with the Lemarie-Meyer wavelet network, the Daubechies2 wavelet network and the GHM multiwavelet network, and the results support the theoretical analysis well. In addition, the results also illustrate that at the jump discontinuities, the approximation performance of the two networks are about the same