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In following, the capability of fuzzy-based sliding-mode control approach, optimized by the genetic algorithm has been used to assure the sustainability of system.
Therefore, in the following, the signals generated by the fuzzy-based sliding-mode control approach, optimized by genetic algorithm, in order to apply to existing engines in structure of the quadrotor robot are shown in Fig. 18.
Subsequently, in the fourth section, the results of the simulations regarding the hybrid fuzzy-based sliding-mode control approach, optimized by the above-referenced genetic algorithm on UAV quadrotor model have been described and finally the last section is dedicated to conclusions.
In order to direct a sample of quadrotor robot automatically, in this paper, the capacities of hybrid fuzzy-based sliding-mode control approach optimized by genetic algorithm was used.
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The modeling approach optimized charge balance by calibrating estimates of mole site density in the DOC (dissolved organic carbon) of surface and pore waters with estimates of triprotic acid dissociation constants.
Also, the adaptive pre-paging approach is optimized by increasing the search space against faulted pages.
In addition, the parameter settings of the proposed approach were optimized by using the Taguchi method.
The process was mathematically modeled using central composite design approach and optimized by quadratic regression model with ANOVA (analysis of variance) for showing the relative significance of the factors.
On the other hand, the decision boundary for classes in a discriminative approach is optimized by considering all positive and negative samples.
After we obtain the eigen tensors by MPCA, we can treat the eigen tensors as input samples, and then we use the approach of SVM optimized by improved FOA to train classification model of pancreatic cancer image.
This paper introduces a two-scale topology optimization approach by integrating optimized structures with the design of their materials.
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