Thus, a set of PI controllers which attain desired performances can be found using the graphical relations.
Also, the graphical relations between predicted and observed data are shown in Fig. 1a, b, c, respectively.
A frequently used approach to determine the relationships of nonlinear data is response curve analysis, which provides graphical relations between individual independent variables and the output variable (product concentration), assuming average values for the other input variables.
A graphical representation of relation between P f and P f f and the index classes (i.e. index values) is depicted in Fig. 3. To accommodate uncertainty in floating point computations and to allow the user to loosen the requirements for the interior cells, we introduce the interior limit i l and optionally use the following condition: lvert P_{f} - 1 rvert < i_{l}.
The FCA largely recapitulated observations based on nuclear differentiation and allowed a graphical illustration of relations among groups of parasitoids as defined in Table S3 [Additional file 5].
We used the empirically observed relations between drugs and ADRs, the mapping of target proteins into pathways, and the virtual normalized docking results of drugs onto experimentally determined human protein structures to construct a graphical representation of these relations (Figure 1).
We used Pathway Studio 6.01 for a graphical output of the relations among the genes in each group.
Liaison or connection graphs depict physical mates between components in a graphical representation but do not incorporate any precedence relations or order of assembly or disassembly of components.
Finally, we infer the sequential or cascade (causal) relations between temporal blocks by a graphical model (e.g., partial correlation) among two groups of genes.
In addition to the graph presentation, the relation among these entities can also be a graphical one, as shown in Figure 5. Figure 5 The selected entities (left) and the oriented graph of their mutual connections (right).
