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Finite-time stabilization is guaranteed under a so-called minimum rule algorithm; for each HDS mode, the state space is divided into different control regions and a switching control rule is constructed to switch between controllers whenever a boundary is reached.
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Score level fusion with minimum rule achieved better results.
They used a sum rule, a product rule, a minimum rule, a maximum rule, a median rule, and majority voting as the basis for the classifier combination scheme.
People use different methods such as majority voting, weighted voting, sum rule, mean rule, product rule, maximum rule, minimum rule, correlation and mutual information to build the combiner.
We found that either a harmonic average or minimum rule works best.
The comparisons among these three algorithms show us: (1) positive region preservation based algorithm can keep the decision rules supported by lower approximation region unchanged; (2) decision-monotonicity based heuristic algorithm can obtain a reduct with more positive decision rules and higher classification accuracy; (3) cost minimum based algorithm can generate a reduct with minor cost.
The rule induction algorithm is based on the Apriori algorithm.
We used the minimum spanning tree algorithm implemented in Bionumerics v.5.1 (Applied Maths NV) with priority rules set at first link genotypes that have maximum numbers of single-locus variants and then maximal numbers of single-locus variants and double-locus variants.
A rule-in and rule-out algorithm (12) was used for the FPG to predict GDM.
Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.
Numerous rules were generated with the CART and C4.5 rule extraction algorithm.
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