improving accuracy of dgps correction prediction in position domain using radial basis function neural network trained by pso algorithm

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ID: 149661
2017
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Abstract
Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Function (RBF) has been developed. In many previous works all parameter of RBF NN are optimizing by evolutionary algorithm such as Particle Swarm Optimization (PSO), but in our approach shape parameter and centers of RBF NN are calculated in better way, in addition, search space for PSO algorithm will be reduced which cause more accurate and faster approach. The obtained results show that RMS has been reduced about 0.13 meter. Moreover, results are tabulated in the tables which verify the accuracy and faster convergence nature of our approach in both on-line and off-line training methods.
Reference Key
mosavi2017iranianimproving Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;M. R. Mosavi;A. Rashidinia
Journal international journal of corrosion
Year 2017
DOI
DOI not found
URL
http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-20&slc_lang=en&sid=1
Keywords
prediction particle swarm optimization accuracy electrical engineering electronics nuclear engineering

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