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Next, the proposed models architecture is discussed in "The proposed model" section with full details about the model's components, inputs and outputs.

The best model architecture is shown in Table 3.

The proposed hybrid modelling architecture is validated in the field of rotating machinery using time-domain and frequency-domain analysis.

The model architecture is flexible permitting updates as new data become available.

Each model architecture is estimated k = 20 times, and because there are eight different neural network-based model architecture to be estimated in the study, the total number of estimated models is 160; whereas, the selected 8 models (based on the lowest MSE error criteria) will be reported in the study.

The detailed model architecture is illustrated in Figure 3.

An overview of the model architecture is depicted in Figure 2. The first component comprises initial feedforward processing.

Our model architecture is inspired by biological mechanisms and is based on neural representations of early visual cortex.

A graphical overview of our model architecture is depicted in Fig. 1.

Our first model architecture is motivated by observations that as one progresses from sensory to prefrontal areas in the primate brain, neurons receive an increasing number of excitatory connections from their neighbors (Wang, 2001; Elston, 2007; Wang, 2008).

The full model architecture is shown in figure 4. When assessing the specificity of the DSE matrices, the only parameters that were optimized were the background emission probabilities (b1 = b2 = b3 for model 4A and 4A and b1 = b3 for 4B) and transitions (p1 and p3 in both models), while the weight matrices and Gaussian spacer parameters were kept fixed.