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Feasibility of the algorithms is proved through practical simulations.
The cost of refining the algorithm with local data or including more clinical data would be a decrease in the generalizability or feasibility of the algorithms.
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Provided an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and the asymptotic stabilization is established.
This part of the experiment was designed to verify the feasibility of the algorithm.
Simulation results demonstrate the feasibility of the algorithm in terms of accuracy and convergence speed.
Some simulating results are presented to validate the correctness of the analysis and the feasibility of the algorithm.
A numerical differentiation example is used to investigate the validity and feasibility of the algorithm and the code.
Plenty of results verify the feasibility of the algorithm for ultra-high speed protection of EHV power transmission lines.
A simulation example, which is part of an aircraft product development process, shows the feasibility of the algorithm.
We designed experiments to verify the three sub-algorithms in the emergency task planning algorithms for combinatorial strategy selection and prove the overall feasibility of the algorithm respectively.
In addition to the algorithm efficiency, we consider the effectiveness and the feasibility of the algorithm in distributed and heterogeneous data environments [11 15].
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