The basic SOM is an artificial neural network that, on the basis of the information contained in a multi-dimensional space, generates a space of lesser dimensions.
Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions for each of them and then rejects instances that have unlikely differences (residuals) between predictions and measurements.
Thus, Eq. (18) can generate a space of h models.
The end data point 〈M2, Pc2, Pn2〉 was moved in all three dimensions to generate a space of likelihood values.
To generate a space of ATP induced R1 hexamerization models, the first step is to pick a full model and the second step is to apply K hypotheses to it [ 25].
The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before.
We show that this antagonistic selection across life-stages generates a neutral space of equivalent co-adapted strategy sets individuals may express as offspring and as parents.
Now we introducing the concepts of a generating space of a b-dislocated quasi-metric family (simply '(G_{bdq} -family') and a G_{bdq} -familye of and-dislocated metric family (simply '(generatingmily').
In 1997, Chang et al. [22] introduced the definition of a generating space of a quasi-metric family and established some interesting fixed point theorems and coincidence point theorems in the generating space of a quasi-metric family.
In this section, we derive some fixed point theorems with examples in the context of a generating space of a b-dislocated metric family.
If we take (d_{alpha} x,y)=d x,y)) for all (alphain 0,1]) and (x,yin X), then ((X,d)) is a generating space of a quasi-metric family.
