In this article, we have seen another type of Artificial Neural Network called Recurrent Neural Network; we have focused on the main difference which makes RNN stands out from othertypes of neural networks, the areas where it can be used extensively, such as in speech recognition and NLP(Natur...
To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) ...
A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural networkData-drivenFault diagnosisLSTMTime-varying working conditionNormal operation of bearing is the key to ensure the reliability and security of rotary machinery, so that bearing fault ...
Tensorflow library integrates various APIs to construct deep learning architectures such as convolutional or recurrent neural networks. The tensorFlow framework is based on the computation of dataflow graphs. These graphs enable developers to represent the development of a neural network. The tensorFlow fra...
Capacity limits can be understood in terms of recurrent neural networks. View article Journal 2013, Trends in Cognitive SciencesSteven J. Luck, Edward K. Vogel Chapter Aging effects on cognitive and noncognitive factors in creativity Working memory and aging As mentioned earlier, in Chapter 3, wor...
These effects also emerge in a recurrent network model with slow excitation, subject to STF, and broad inhibition (Fig. 1E). This network represents the memory of the presented target as a bump of neural activity, which drifts in the direction of the target presented on the previous trial ...
Zhang J, Jiang Y, Wu S, Li X, Luo H, Yin S (2022) Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliab Eng Syst Saf 221:108297 Article Google Scholar Liu S, Jiang H (2023) Engine remaining useful life prediction ...
An LSTM model basically consists of an input gate, an output gate, a forgetting gate and a memory (recall) gate [55]. Long short-term memory (LSTM) is a synthetic recurrent neural network (RNN) architecture used in deep learning. Unlike standard feed-forward neural networks, LSTM has feed...
D3D12 - Metacommands - Recurrent Neural Network PICT D3D12 - Metacommands - Reduction D3D12 - Metacommands - Reduction PICT D3D12 - Metacommands - Reflection Metadata D3D12 - Metacommands - Region of Interest Pooling D3D12 - Metacommands - Region of Interest Pooling PICT D3D12 - Metacomman...
derived unit), this definition can be interpreted as the effort of a brain region needed to steer the activity pattern of itself and its connected brain regions into the desired final activation state, for example by tuning their internal firing or activity patterns by recurrent inhibitory ...