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. Author manuscript; available in PMC: 2024 Oct 5.
Published in final edited form as: IEEE Trans Neural Netw Learn Syst. 2023 Oct 5;34(10):6983–7003. doi: 10.1109/TNNLS.2022.3145365

Fig. 5.

Fig. 5.

The implementations of RNN models are determined by the label structure of each signal sample. (a) shows a signal sequence with a sequential label. The general applied RNN could be designed in (c). Sometimes a signal sequence could only have one annotated label, as shown in (b), and the RNN could be designed in the form of (d). Although (c) and (d) show one-layer unidirectional RNN, multiple stacked layers or bidirectional RNN are also adoptable.