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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
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ISBN:
9780791859902
No. of Pages:
1400
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
113 Using Neural Networks for Dynamic Vehicle Navigation Using Integrated GNSS / INS Your Romania
By
Bădescu Gabriel
,
Bădescu Gabriel
The North University of Baia Mare
, Department Geodesy of Mines, Victor Babeş, str nr. 62/A, Baia Mare
, Romania
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Stefan Ovidiu
,
Stefan Ovidiu
The North University of Baia Mare
, Department Geodesy of Mines, Victor Babeş, str nr. 62/A, Baia Mare
, Romania
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Pop Nicolae
,
Pop Nicolae
University of Agricultural Sciences and Veterinary Medicines
, Manăştur, str. nr 3-5, Cluj-Napoca
, Romania
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Veres Samuel Ioel
,
Veres Samuel Ioel
The Petroşani University
, Faculty of Mines, Petroşani
, Romania
.
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Ortelecan Mircea
,
Ortelecan Mircea
University of Agricultural Sciences and Veterinary Medicines
, Manăştur, str. nr 3-5, Cluj-Napoca
, Romania
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Bădescu Rodica
Bădescu Rodica
The North University of Baia Mare
, Department Geodesy of Mines, Victor Babeş, str nr. 62/A, Baia Mare
, Romania
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Page Count:
4
-
Published:2011
Citation
Gabriel, B, Ovidiu, S, Nicolae, P, Ioel, VS, Mircea, O, & Rodica, B. "Using Neural Networks for Dynamic Vehicle Navigation Using Integrated GNSS / INS Your Romania." International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011). Ed. Ming, C. ASME Press, 2011.
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Recently, the newest methods based on Artificial Intelligence (AI) are used in order to provide reliable positioning information for various navigation applications for ground vehicles with the help of (GNSS) technology, with integrated inertial navigation system (INS).
All existing methods based on Artificial Intelligence (AI) rest upon the INS system error regarding the correct INS operation, at certain times, and do not take into account the error relation for the past INS values. This study presented, therefore, suggests the use of entry-Delayed Networks Neural (IDNN) to model both the INS position and velocity errors based on past samples of the...
Abstract
Keywords:
Introduction
Dynamic Neural Networks Used by the INS and IDNN
Methodology
Road Test Experiment
Results and Discussions
Conclusions
Reference
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