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In order to perform parameters' identification on full-scale Desiccant Evaporative Cooling (DEC) units, an ad hoc measurement apparatus has been developed and tested, along with a suitable identification procedure.
Thus, in this paper, we perform parameters optimization for the PSO algorithm, based on genetic algorithm, in order to increase the classification accuracy.
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We also perform parameter analysis on performance of the two channel models.
We have shown that different choices of measurements lead to varied ability to perform parameter estimation.
Therefore, doe method allows us to perform parameter studies that could be consisting of multiple variables.
Grid search algorithm is employed to perform parameter selection for these techniques.
The majority of evolutionary learning techniques have been designed to perform parameter optimization.
The modeling accuracy, model interpretability, and procedures to perform parameter with relative importance are presented.
In order to perform parameter identification, the forward simulation method is coupled to standard optimization tools.
In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN).
Accordingly, we perform parameter estimation using a penalized partial likelihood criterion [17] that forces the solution to the estimation problem to have small L2-norm.
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