Comparison of L-1 Norm and L-2 Norm Minimisation Methods in Trigonometric Levelling Networks
Küçük Resim Yok
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
UNIV OSIJEK, TECH FAC
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The most widely-used parameter estimation method today is the L-2 norm minimisation method known as the Least Squares Method (LSM). The solution to the L-2 norm minimisation method is always unique and is easily computed. This method distributes errors and is sensitive to outlying measurements. Therefore, a robust technique known as the Least Absolute Values Method (LAVM) might be used for the detection of outliers and for the estimation of parameters. In this paper, the formulation of the L-1 norm minimisation method will be explained and the success of the method in the detection of gross errors will be investigated in a trigonometric levelling network.
Açıklama
Anahtar Kelimeler
linear programming, measurements with gross error, simplex method, trigonometric levelling networks
Kaynak
TEHNICKI VJESNIK-TECHNICAL GAZETTE
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
25
