Results 211 to 220 of about 8,808,141 (242)

Total least mean squares algorithm

IEEE Transactions on Signal Processing, 1998
Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm ...
Da-Zheng Feng, Zheng Bao 0001, Licheng Jiao   +2 more
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Total least squares in robot calibration

1997
The role of input noise is seldom considered in robot calibration. The methodology of total least squares may be applied to handle both input and output noise in robot calibration. Experimentally, we apply this method towards joint torque sensor calibration, and towards kinematic calibration of a redundant parallel-drive spherical joint in a variant ...
John M. Hollerbach, Ali Nahvi
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Total Least Squares

2018
The chapter treats total least squares (TLS), which in statistics corresponds to orthogonal regression. Some different extensions are discussed, including ways to show how uncertainties in different matrix elements may be related or correlated. The application of TLS to identification of dynamic systems is also treated.
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The Total Least Squares Technique

1995
Abstract Notice that AXo = (AA+)B and, as AA+ is just the orthogonal projector onto Im A, Xo is the minimum norm solution of the consistent system AX = (AA+)B obtained by projecting Im B onto Im A. Thus, in this process, the subspace Im A plays the pivotal role and the “right-hand side” matrix B is “adjusted” to produce a solvable ...
Peter Lancaster, Leiba Rodman
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On the Significance of Nongeneric Total Least Squares Problems

SIAM Journal on Matrix Analysis and Applications, 1992
Consider an overdetermined system \(AX=B\), where \(A\in\mathbb{R}^{m\times n}\), \(B\in\mathbb{R}^{m\times d}\). Any \(X\in\mathbb{R}^{n\times d}\) is called a total least squares solution of this system, provided \(X\) solves \(\widehat A X=\widehat B\), where \([\widehat A,\widehat B]\in\mathbb{R}^{m\times(n+d)}\) minimizes \(\| [A,B]-[\widehat A ...
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Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data

Journal of Computational and Applied Mathematics, 2023
Feng Ding
exaly  

Total Least Squares in Astronomy

2002
Although astronomers have been involved with the development and use of least squares, they have made insufficient use of total least squares. Astronomers, however, have examined alternatives that also permit error in the equations of condition. There exist, nevertheless, problems of astronomical data reduction for which total least squares represents ...
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Least Median of Squares Regression

Journal of the American Statistical Association, 1984
Peter Rousseeuw
exaly   +2 more sources

Perturbation analysis and condition numbers of mixed least squares-scaled total least squares problem

Numerical Algorithms, 2021
Pingping Zhang
exaly  

Deformation Analysis With Least Squares And Total Least Squares Methods

In this study, application of Total Least Squares TLS method in deformation analysis and comparison of its results with the Least Squares LS method was aimed. In this context, GPS observations collected in a landslide area nearby BüyükçekmeceGürpinar landslide region in October 1997 and in March 1998 were processed.
ACAR, Mustafa, AYAN, Tevfik, AKYILMAZ, Orhan   +2 more
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