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Inspired by the CL optimization architecture which periodically selects the best optimal parameters from different layers, we solve the dual function in the physical layer and application layer separately.
Furthermore, the design of experiments methodology (DOE) and the goal design optimization (GDO) technique are used to calculate the best optimal parameters from the laboratory tests.
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In this paper, we present a method for concurrent learning of best strategy and optimal parameters using policy gradient reinforcement learning algorithm.
A higher grey relational grade corresponds to the best optimal setting of process parameters for multi responses.
Furthermore, optimal parameters providing the best possible performance of each configuration have been identified.
Again, through cross-validation, we train each model with its best set of features and the optimal parameters selecting the one that shows the highest precision.
The results of this analysis clearly show that any fixed-points-theory-based conventional method could provide, at best, only locally but not globally optimal parameters.
Then, in each fold of leave-one-out CV, we yield the best classification performance with these combined optimal parameters.
The aim is to find the parameters that give the best (optimal) fit to a set of experimental data, which entails minimizing (optimizing) a cost function that measures the goodness of this fit.
And the optimal parameters might obtain the best comprehensive performance of PHEB for the given Chinese urban driving cycles.
For each algorithm, we found optimal parameters that result in best F1 score.
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CEO of Professional Science Editing for Scientists @ prosciediting.com