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We recall that the continuous space is represented by a bidimensional square of side (L = 1).
The former is represented by a bidimensional square, whereas the latter has been modeled by a directed network.
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Principal component analyses applied only to hydrogen-bonded complexes whose experimental frequencies are known, has revealed that the six-dimensional original space can be accurately represented by a bidimensional space defined by two principal components.
Let us consider a physical field represented by a bidimensional time-varying signal s x,y t), ( x, y ) ∈ R 2 monitored through a grid of N = N 1 × N 2 sensors deployed over a bidimensional covered area, N 1 and N 2 being the number of sensors distributed in the horizontal and vertical dimension, respectively.
Divided by, this is just multiplied by a squared plus k squared.
The SOM models represent a multidimensional dataset by means of a bidimensional matrix of features, which can be explored for analysis and estimation purposes.
The modeling using Self-Organizing Maps (SOM) consists of representing multidimensional input vectors by means of a bidimensional map (Silva et al. 2010).
Different from the conventional ANN, SOM-based models represent a multidimensional data set by means of a bidimensional matrix of features, which may be applied for analysis and estimation purposes.
The reactor behaviour is described by a quasihomogeneous bidimensional mathematical model.
Males are indicated by a square.
A modification for convex functions which is also known as co-ordinated convex functions was introduced as following by Dragomir [19]: Let us consider a bidimensional interval Δ =: [a, b] × [c, d] in ℝ2 with a < b and c < d.
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