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The effectiveness of the present method is demonstrated through its performance in a series of examples with results compared to alternative solutions with excellent agreements.
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These problems are investigated in many works, see [3] [6] for example, with results only about the well-posedness.
Figure 6 presents a practical example with results of SEEDS analysis in the same well project, with 5 phases for three different rigs (with distinct configurations of BOP), with diverse levels of risk exposure, as EDS 0 without RSM is selected.
Examples with numerical results are added to illustrate the results obtained.
In order to illustrate the observer design procedure, several examples with simulation results are presented.
Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network.
Numerical examples with simulation results show that the proposed approach can be useful in practical applications.
Finally, two numerical examples with simulation results are given to show the correctness of the obtained passivity criteria.
The feature recogniser was implemented in C on a PC and tested on a large number of examples with positive results.
As reporteDespiteble 3, the proposed approacompletebtained the besetperfofmances imagesms of both, hit rate and qusedty crinerion.
In this contribution we show several application examples with novel results obtained by using the CBA mode to demonstrate its excellent performance and capabilities for investigating scientific problems in surface science and materials research.
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