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Results show that the particle swarm optimization is superior to the NLP optimization techniques in finding the values of optimizing variables.
Phase-based optimization is superior to the intensity-based optimization while the proposed optimization can generate the least phase errors.
Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.
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These results reveal that, when modeling more complicated shapes, an optimal rotation estimation using manifold optimization techniques is superior.
It was observed that performance of the developed Pareto optimization algorithm is superior compared to desirability function approach.
The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques.
Among the developed models, performance of neural network model trained with particle swarm optimization model is superior in terms of computational speed and accuracy.
The study shows the optimization algorithm is superior and feasible as well as it is better used in the OTU and plays an important part in the practical application.
The simulations reveal that the non-parametric model outperforms all of the selected parametric models in terms of the fitting accuracy and the operational simplicity, and the stochastic heuristic optimization algorithm is superior to the widely used estimation methods.
The results revealed that the proposed VQ SVR model coupled with hybrid GA SQP optimization algorithm is superior to other methods and gives the best prediction for the sulfur content with the highest accuracy (AARE = 0.0745, R 2 = 0.997) and the lowest computation time (CT = 56 s).
When the period is small the intensity-based optimization has less phase RMS error than phase-based optimization, and the proposed optimization has the same tendency with intensity-based optimization but it is superior to the intensity-based optimization with different Gaussian filter sizes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com