The membership degree of the fuzzy set is defined over a domain X which takes the form: mu_{A} {kern 1pt} {kern 1pt} :X to left[ {0,1} right], (4 where each element of X is mapped to a value between 0 and 1.
For each protein P j in the set (where 1≤ j≤ m), its feature value for feature f i, denoted f i j, is mapped to a value d1 if f i j ∈ [ l f i, T i ] and to another value d2 if f i j ∈ (T i, h f i ], where d1 and d2 are two distinct values, chosen from the set {0,1,2,…} (e.g. d1=0 and d2=1).
The evaluation of a given parameter point with the Kalman filter maps to a unique value of the cost function (despite the use of SDEs).
If F(x i )maps to a unique value of y j (i.e. a one-to−one mapping), then the data associated with x i is mapped as attributes of node, y j.
The mapping rules between the labels in the data file, X, and the node names in the active graph, Y, F X)→ Y where X={ x1,…, x N } and Y={ y1,…, y M } can be summarized as: If F (x i )maps to a unique value of y j (i.e. a one-to−one mapping), then the data associated with x i is mapped as attributes of node, y j.
There are some values of for which, thus mapping to a negative value when.
Secondly, we identified the attributes in the INSPIRE schema that can be mapped to a constant value.
In the forecast mode of trained SVR, an input score vector may be mapped to a real value as its fused score.
Using exponential effective signal-to-noise ratio mapping (EESM) technique, a sequence of varying SNRs are mapped to a single value that is strongly correlated with the actual BER.
The memberships are curves that define how each point in the input space is mapped to a membership value between −0.032 to 0.032 and −100 to 100 for 'E' and 'CE' respectively.
The wind speed ranges from a minimum of 5.36 m/s, represented by the blue colour on the map, to a maximum value of 16.55 m/s represented by the red colour.
