Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization
Fuzzy Optimization and Decision Making
When are Simple LS Estimators Enough? An Empirical Study of LS, TLS, and GTLS
International Journal of Computer Vision
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
Overview of total least-squares methods
Signal Processing
Robust constrained receding-horizon predictive control via bounded data uncertainties
Mathematics and Computers in Simulation
A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models
Journal of Scientific Computing
Cutting-set methods for robust convex optimization with pessimizing oracles
Optimization Methods & Software
A subgradient solution to structured robust least squares problems
IEEE Transactions on Signal Processing
Structured Total Maximum Likelihood: An Alternative to Structured Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Spectral Methods for Parameterized Matrix Equations
SIAM Journal on Matrix Analysis and Applications
Brief Robust maximum likelihood estimation in the linear model
Automatica (Journal of IFAC)
Robust estimation in flat fading channels under bounded channel uncertainties
Digital Signal Processing
| Hi-index | 0.01 |
We formulate and solve a new parameter estimation problem in the presence of data uncertainties. The new method is suitable when a priori bounds on the uncertain data are available, and its solution leads to more meaningful results, especially when compared with other methods such as total least-squares and robust estimation. Its superior performance is due to the fact that the new method guarantees that the effect of the uncertainties will never be unnecessarily over-estimated, beyond what is reasonably assumed by the a priori bounds. A geometric interpretation of the solution is provided, along with a closed form expression for it. We also consider the case in which only selected columns of the coefficient matrix are subject to perturbations.