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The model architecture characteristics are presented in Table 1.
The model architecture consists of three tiers.
Level 1 primarily affects the model architecture.
Existing research and program history was used to determine the model architecture and determinant variables.
This approach assumes therefore minimal constraints on the model architecture and cell characteristics.
Furthermore, subsystems and decentralized controls are applied in the model architecture.
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The model architectures of the proposed ANN models and their training results are reported in Table 4 in terms of MSE and ρ correlation statistics for the training and test samples.
This modification to the modeling architecture greatly improved the accuracy over a one CV model, which was incapable of capturing the stack vapor dynamics.
The ANN model architecture refers to the layout of neurons and the number of hidden layers, as shown in Fig. 1.
The optimized model outperformed the previous model architecture with up to 55% performance improvement.
To mention only few of them: the JDL fusion model architecture, the Waterfall model, the Intelligence cycle, the Boyd loop, the LAAS architecture, the Omnibus model, Mr. Fusion, the DFuse framework, and the Time-Triggered Sensor Fusion Model.
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