The original data of this model is fed to the two-layer LSTM and then to the convolutional layer, which enables the LSTM to learn the temporal dynamics on different time scales based on the learned parameters, resulting in better accuracy. And the use of a global average pooling layer ...
Fig. 1 depicts the standard swashplate pump structural diagram. It consists of a rotating shaft driven by an Proposed swashplate pump fault detection approach A BiLSTM-based adaptive signal fusion framework is presented to develop an improved classification model for swashplate pump fault detection, ...
Eck, Douglas, and Juergen Schmidhuber. “Finding temporal structure in music: Blues improvisation with LSTM recurrent networks.” In Proceedings of the 12th IEEE workshop on neural networks for signal processing, pp. 747-756. IEEE, 2002. (Year: 2002). ...
(Zhu et al., 2024) demonstrated that the BiLSTM model outperforms the LSTM model in time-series prediction. However, this approach has the limitation that hyperparameter settings rely heavily on empirical methods, requiring multiple tests and analyses to identify the most effective parameters. ...
For sequence modeling of signals, hidden markov model (HMM), recurrent neural network (RNN), and more recently developed long short-term memory (LSTM) have been widely utilized owing to enabling memorizing the output of the last moment for circulated self-updating and adapting. They are usually...
Figure 4. Framework diagram of Deep BiLSTM. The forward and backward outcomes of the LSTM units are represented by the symbols ℎ𝑡←ht← and ℎ𝑡→ht→. The combination of these two LSTM units forms the output 𝑣𝑡vt. The fundamental concept underlying the RNN resides in the ...
Figure 1. One-way Bi-LSTM structure diagram. The Bi-LSTM neural network is now widely used in many scenarios in NLP and good results have been achieved. Reference [18] proposed a multi-scale deformable CNN to capture the non-consecutive n-gram features by adding an offset to the convolut...
In order to improve the accuracy of Chinese text error detection, we combined the advantages of the LSTM neural network model [31] and CRF technology [32], and proposed our Chinese error detection model BLSTM-CRF. The model consists of three layers: an embedded layer, a bidirectional LSTM la...
A brief block diagram of the BLSTM structure is shown in Figure 6. In this figure, the BLSTM first processes the forward LSTM before processing the backward LSTM, and the activation function produces the result [15]. Hence, this system contains the past and future input data. Figure 6. Bi...
Figure 2. Box block diagram. Figure 3. LSTM network basic unit. Figure 4. BiLSTM network structure. Figure 5. Predictive model flow. Figure 6. Wind farm wind power situation: (a) March wind power data; (b) June wind power data; (c) September wind power data; (d) December wind powe...