an iterative but fast satellite selection algorithm is proposed based on the Sherman–Morrison formula and the maximum-volume-tetrahedron method. The algorithm is verified using measured data from the UB4B0 GNSS chip produced by Unicore
Population represents the number of chromosomes that participate in the genetic algorithm. One of the individuals or chromosomes of the population will be considered as the solution of the genetic algorithm. The population’s size directly affects the genetic algorithm’s time complexity. Every iteratio...
GQBWSSA, proposed in this paper is depicted in the flowchart as shown in Algorithm 1. The algorithm achieves a more rational initial distribution of the population through the use of an good point set and quantum encoding. The utilization of adaptive weights is what allows for the acceleration ...
The satellite selection algorithm based on the Sherman–Morrison formula is then used to add and select the other visible satellites. The specific steps of the smaller-GDOP-value method in the co-selec- tion process of visible satellites can be described as Fig. 3 Flowchart of the ...
[8] introduced ADAPT, an approximate algorithm specializing in QoS multipath selection to find paths with minimal cost while adhering to the end-to-end delay constraints. The strength of the algorithm iteratively refined the obtained results to minimize an additional objective while satisfying other ...
General filtering techniques flowchart Full size image 2.2Wrapper techniques In case of wrapper techniques, a classification algorithm is used to evaluate the candidate channel subsets, which are generated by a search algorithm as shown in Fig.7, in whichAdenotes a classifier, andγbestrepresents the...
Algorithm the longest common substring of two strings Align output in .txt file Allocation of very large lists allow form to only open once Allow Null In Combo Box Allowing a Windows Service permissions to Write to a file when the user is logged out, using C# Alphabetically sort all the pro...
Fig. 2. A flowchart of the genetic algorithm. 2.3.1. Non-Dominated Sorting Genetic Algorithm III (NSGA-III) NSGA-III is a pareto and elitist reference-based GA for many-objective optimization problems (Deb & Jain, 2013). It is elitist because it is designed to preserve the set of best...
In addition, due to irrelevant or redundant features, excessive features not only reduce the efficiency of the learning algorithm but also may lead to overfitting, resulting in a decrease in the model's generalization ability [1]; this will harm the classification accuracy. As a data ...
A feature selection method based on three-way interaction information and KS test is proposed, and its pseudocode is shown in Algorithm 1. IIM Plus KS consists of two parts: In the first part (lines 1–17), the candidate feature set X, the selected feature set T and the ranked feature ...