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This work studies the effect of different models on the performance of multistep model predictive control (MMPC) via simulation examples and bench-and pilot-scale experiments. The models used in the study are two common types of artificial neural networks (ANNs), namely, feedforward networks (FFNs) and external recurrent networks (ERNs). The steady-state(More)
In genetic algorithm (GA), there are two main methods of determining trial candidates: crossover and mutation. While crossover directs search between fit candidates, mutation plays a role on jumping out local optimal. In molecular docking calculations, it is desirable to chart as much unexplored search space as possible. Therefore it is desirable that(More)
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