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Simulation-based results show that the proposed scheme has good performances compared to existing feedback schemes.
Neural network[1] approaches have been extensively used in the past few years for nonlinear MIMO system modeling, identification and control where they have shown very good performances compared to classical techniques[2 6].
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Additionally, the proposed algorithm has a good performance compared to CPLEX in reaching optimal/near optimal solutions.
Simulation results show that the proposed algorithms can achieve a good performance compared to the benchmark algorithm.
We propose a multi-agent architecture that fit the real emergency systems, and that aims at keeping good performance compared to the centralized solution.
Despite subjecting the DFIG to various modes of operations and extreme faulty conditions, the over currents of the rotor and stator did not exceed 10%, which is a very good performance compared to existing approaches.
The obtained results indicate that good performance, compared to that of GC current mirrors, may be obtained by combining both standard and graded-channel transistors, rather than using the same channel engineering for all devices in the circuit.
ACM claim extremely good performance compared to more traditional alternatives.
OA shows good performance compared to other methods.
In many works [12], complex algorithms, like the Random Forest, have demonstrated a very good performance compared to simple classification algorithms.
These methods achieve very good performance compared to BSS but require a considerable bit overhead (at least 5 kbit/s per source according to [14]).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com