چكيده به لاتين
The Global Positioning System (GPS) is able to accurately measure the exact time, altitude, length, and latitude of any desired point on the Earth's surface. The signals received on the Earth's surface are very weak and will not be useful for precise works. The parameter for calculating the positioning errors is defined as Geometric Dilution of Precision GDOP, the smaller one will get the more accurate position. For GDOP calculations, the reverse matrix method needs high processing and computational volume, and access to the position will not be possible in the shortest possible time. Therefore, the proposed design will be used by the Multilayer Neural Network MLP (MLP NN) methods, which will obtain the GDOP approximation. The MLP NN requires the determination of the weights and biases of its nodes, which will be developed using the methods of evolutionary algorithms. There are several algorithms in this thesis that will use the Bio-based Biographical (BBO) method and demonstrate its capabilities and advantages over other algorithms. In the following, valid global datasets such as Iris, Balloon, Breast, BankNote and Seeds will be evaluated to confirm the claim that BBO will be introduced as a more accurate algorithm with fewer errors for MLP NN training. The proposed method will also be used to test real GPS dataset. The results of the proposed design will indicate that the proposed method has a GDOP classification rate of approximately %83 for the BBO algorithm, %79 for the PSO algorithm, %80 for the GA algorithm, %66 for the ACO algorithm, %48 for the ES algorithm, and %60 for PBIL algorithm, and the BBO algorithm will be able to categorize GDOP and achieve the good GPS position with the most accuracy and the least error.
