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d Membership functions of the node priority .
Wang and Rong defined the relevant functions of the node degree as the node load [18, 19, 20, 21].
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In contrast, in functional networks the structure S is fixed and the input-output functions of the nodes are fit such that the overall model represents the data (Figure 1a, b). Figure 1 Structures of functional networks.
sop – Converts local functions of the nodes to SOPs.
bdd – Converts local functions of the nodes to BDDs.
aig – Converts local functions of the nodes to AIGs.
espresso – Minimizes the current local functions of the nodes by applying SOP minimizer Espresso-MV.
In neural networks the input-output functions of the nodes are fixed up to a small set of parameters and the structure S is used for the adaption to the data.
Based on this assumption, we derived a metric which is a function of the node position.
In Figure 6(b), the packet error rate (PER) is shown as a function of the node orientation.
It was proven in[6] that the MSE in (26) is a decreasing function of the node density δ.
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