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The model has been implemented using both Interactive and Non-Interactive flamelet strategies.
Behavior arbitration has been implemented using both fuzzy logic and utility fusion.
Full custom designs have been implemented using both 32 nm CMOS PTM models and 30nm FinFET models to justifiably evaluate and compare their performance.
Statistical pattern-matching has been implemented using both the expectation-maximization algorithm and the Gibbs sampler.
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The networks were implemented using both conventional maximum likelihood and Bayesian evidence based training algorithms.
The MA2LVQ is implemented using both low-complexity A2LVQ and ordinary A2 architectures.
These proposed algorithms are implemented using both synthetic and real datasets.
Simulations are implemented using both the implicit and explicit codes respectively using non-linear finite element (FE) package ABAQUS.
The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine.
Second, the model can be implemented using both shrinkage methods as well as variable selection methods.
Models were implemented using both SAS version 8.2 (SAS Institute, Cary, NC) and S-Plus 6 (Insightful Corporation, Seattle, WA).
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