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The success of deep learning proves that deep models are able to achieve much better performance than shallow models in representation learning. However, deep neural networks with auto-encoder stacked structure suffer from low learning efficiency since common used training algorithms are variations of iterative algorithms based on the time-consuming gradient descent, especially when the network structure...
Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization...
Identification and classification of fault signal in power systems is an important task. For the pattern recognition of disturbance signal, this paper presents five benchmarks of disturbance signal by MATLAB; Then, feature vectors of the disturbance signal which are extracted by the wavelet packet transforms; The vectors can be recognize by SVM multi — classifier. Numerical results show this approach...
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