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Energy demand forecasting using ridgelet neural networks Boosted Beluga whale optimization
Engineering Research ExpressAbstract Deep learning has become increasingly popular in energy demand forecasting, due to its ability to efficiently process large amounts of data with improved accuracy. By utilizing a deep learning approach, energy demand forecasting models can capture and learn the complex patterns and relationships that exist within energy ...
Shirui Guo +5 more
openaire +1 more source
2006
In this paper, we propose a multiscale feature extraction method of hand-vein patterns based on ridgelet transform and local interconnection structure neural networks. In order to restrain the noises and emphasize the hand-vein pattern in the image, we perform the multiscale self-adaptive enhancement transform based on the ridgelet transform to the ...
Yu Zhang, Xiao Han, Si-liang Ma
openaire +1 more source
In this paper, we propose a multiscale feature extraction method of hand-vein patterns based on ridgelet transform and local interconnection structure neural networks. In order to restrain the noises and emphasize the hand-vein pattern in the image, we perform the multiscale self-adaptive enhancement transform based on the ridgelet transform to the ...
Yu Zhang, Xiao Han, Si-liang Ma
openaire +1 more source
2018
This article proposes a human emotion classification model using ridgelet transformation-based emotion image features and with back propagation artificial neural network. Ridgelet transformation method was applied to face images around the points of interest area.
Ramachandran Vedantham +2 more
openaire +1 more source
This article proposes a human emotion classification model using ridgelet transformation-based emotion image features and with back propagation artificial neural network. Ridgelet transformation method was applied to face images around the points of interest area.
Ramachandran Vedantham +2 more
openaire +1 more source
A linear ridgelet network approach for fault diagnosis of analog circuit
Science China Information Sciences, 2010exaly

