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The regression between the network outputs and the experimental data was more than 0.95.
From assessment of the ANN, it is found that the network including two hidden layers with 4 neurons in every layer results in the least difference between the network outputs and the experimental data, providing the best performance.
Fig. 10 Regression analysis between the network outputs and the optimization targets based on the MLPNN Fig. 11 Regression analysis between the network outputs and the optimization targets based on the RBFNN.
The neural network, trained by the 0.10 target error, presents a 0.086 error and a 0.850 correlation between the network outputs and the training sets.
In addition to the mean squared errors, the correlations between the network outputs and the training data increased to 0.928, 0.983, and 0.995 in the target errors 0.05, 0.01, and 0.005, respectively.
Furthermore, as represented in Fig. 3, the distributions of the network errors, the difference between the network outputs and the training data sets, showed the decreases as the target errors decrease from 0.10 to 0.005.
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The regression between the network output and the corresponding target is equal to 100% which means a high accuracy.
These computations are based on the minimization of the quadratic difference between the network output and Residence Time Distributions (RTDs) obtained through CFD simulations.
The results show that the average relative errors between the network output values and the calculated values of off-design condition are less than 1%, which verify the accuracy, feasibility and validity of this method.
This process is repeated until the error between the network output and desired output (PR measurements) meets the prescribed requirement.
This process is repeated until the error between the network output and desired output (radar measurements) meets the prescribed requirement.
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between the network players
between the model outputs
between the power outputs
between the sensor outputs
between the network modules
between the network objects
between the beamformer outputs
between the network parameters
between the macroscopic outputs
between the image outputs
between the cardiac outputs
between the system outputs
between the amplifier outputs
between the network synchronizations
between the network elements
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