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In this derivation, Approximation 1 is used once.
The reason for the minimum error when using linear approximation 1 is the increasing external force in the experiment.
In the zero approximation, (1) gives the equilibrium condition for a plasma configuration in steady state (∂/∂t = 0) (5).
System approximation 1 is expected to even perform better than the approximation 2. However, due to its computational overhead, it is not desirable.
The following discussions focus on how to determine the dimensions D′ and F′ of the ICT and the performance of the HRTF tensor subspace approximation: 1.
The CBP and CDP values of the system approximation 2 are slightly lower than that of the system approximation 1, since in system approximation 2, we will drop all the pre-empted BE calls, and therefore, the BE load in the system approximation 2 is slightly smaller than that of the system approximation 1.
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Applications include pattern recognition and function approximation [1].
The optimal variable node degree distributions are found through density evolution using the Gaussian approximation [1].
This effect is often treated within the BCS approximation [1, 2, 3, 4, 5, 6, 7, 8].
The optimal degree distribution of irregular codes can be found by density evolution (DE) using the Gaussian approximation [1].
The code is designed by optimizing the variable node degree distribution using density evolution under Gaussian approximation [1].
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