- To estimate suitable petrophysical properties, would it will be useful to use CNN or GRU algorithms alone?
- Or, will this estimate will be appropriate when the two methods are combined?
- Convolutional_Neural_Network: In deep learning, a CNN is a class of artificial neural networks, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. Counter-intuitively, most CNN's are only equivariant, as opposed to invariant, to interpretation.
- Gated_Recurrent_Units: GRU is a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.
Qamar Ul Islam
Erfan Rahimi
Because of its great accuracy, CNNs are employed for picture categorization and identification. The CNN uses a hierarchical architecture to form a network, similar to a funnel, and then outputs a fully-connected layer where all the neurons are connected to each other and the output is processed.
I suggest this link for deep insight: https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
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Aref Wazwaz
Dear Erfan Rahimi . See the following useful RG link: Article A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid...
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