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International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 2 Issue 9, 2013.
Abstract: Comparative study among Least Square Method: LSM, Steepest Descent Method: SDM, and Conjugate Gradient Method: CGM for atmospheric sounder data analysis (estimation of vertical profiles for water vapor) is conducted. Through simulation studies, it is found that CGM shows the best estimation accuracy followed by SDM and LSM. Method dependency on atmospheric models is also clarified.
Kohei Arai , “Comparative Study Among Lease Square Method, Steepest Descent Method, and Conjugate Gradient Method for Atmopsheric Sounder Data Analysis” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(9), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020906
@article{2013,
title = {Comparative Study Among Lease Square Method, Steepest Descent Method, and Conjugate Gradient Method for Atmopsheric Sounder Data Analysis},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.020906},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020906},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {9},
author = {Kohei Arai }
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.