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This MCDM method is based on two processes, the weights assessment of each criterion using the FAHP and the ranking estimation of each possible solution by means of one of the following techniques: the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Multiplicative Exponential Weighting (MEW), or the Grey Relational Analysis (GRA) [14 19].
With such processes, the weights given to the loci for the sum or averaging are uniform.
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The microcomputer processes the weight of the empty seats, and weighs them again with the driver or passenger.
In the training process, the weights of the Q-network were saved regularly in every 300 steps of iteration.
During the learning process, the weights are modified so that the net converges on learning the desired behavior.
To accomplish the learning process, the weights need to be adjusted until the output of the network is approximately equal to the desired output.
During the training process the weights are updated in such a way that the square sum of the difference between real output (desired output) and the output of the network is minimized.
In the case of something as complex as a model-selection process, the weights effectively measure a state of belief about the set of candidate models.
And, in the end, she did not feel or process the weight of responsibility.
During the whole process, the weight concentration of NS was 0.2%.
At the end of the learning process, the weight at each node (SOM neuron) is its estimated location.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com