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The lower value for the weight of the second position is 0.01, and the sum of the both places is equal to one.
We set the maximum (3.0) and minimum (0.0) value for the weight of the van der Waals attractive potentials, electrostatic attractive potentials, electrostatic repulsive potentials and the polar desolvation potentials.
Each observation (x_{n}) is associated with a weight (Clambda _{n}) and a smaller value for the weight implies that the correspondent observation is allowed to have a bigger error.
As an example, for Hebbian learning, for activations based on stimuli that return from time to time an analysis can be made about when there is enough stimulation over time to achieve or maintain a value for the weight ({upomega }) of some connection.
However, we set the maximum (0.001) and minimum (0.0) value for the weight of the van der Waals repulsive potentials, on account of inherent defect in the equation function to calculate the van der Waals repulsive potentials and we want to reduce the impact of the van der Waals repulsive potentials on the whole scoring function.
The default value for the weight of the reactions was one.
Similar(52)
In this contribution, the neural network value is used as an approximate value for the weights.
Similarly, the default value for the weighting coefficient for VM termination w t is 0.75, which gives more weight to the proportional factor for termination P T (k).
To do that, v computes the best possible weight of the clique that it can create with u using the weight of the edge {v,u} and the maximum possible value for the weights of remaining edges w max.
Thus a high initial value for the weights increases the resistance to discharges outside the pattern, leading to a better performance.
The measured values for the weight- % of oxygen behaved quite opposite (see Fig. 4b).
More suggestions(15)
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