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Exact(49)
Each particle in the population has one NPV value and corresponding penalty values.
Fig. 6 Weighted SP recovery based on the diagnosed supports with different penalty values.
DSPC updates penalty values until they satisfy the threshold-based optimality condition.
Moreover, the convergence rate of the EDSPC algorithm is much faster than DSPC, since DSPC continues updating the penalty values even after the optimal solution is found for the current penalty values.
As shown in Figure5, the EDSPC algorithm approaches the optimal utility, when the initial penalty values are carefully chosen.
Penalty values are enforced when the SUs interfere with PUs, which improve the SU learning and the overall spectrum usage.
Similar(11)
where β1and β2 are normalized penalty factors and enable penalty value to be adjusted dynamically.
In Fig. 5, the penalty value σ in the weighting matrix is 0.1.
Each property is assigned with an index or penalty value based on the degree of potential hazards.
The path similarity serves as a penalty value to the support value of a cluster of messages.
The infeasibility penalty value is measured by term ( left( {mathop sum limits_{s in varOmega }^ p_{s} delta_{so} } right) ).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com