Maximum likelihood estimators and worst case optimal algorithms for system identification
Systems & Control Letters
Stable adaptive systems
A class of algorithms for identification in H∞
Automatica (Journal of IFAC)
Worst-case control-relevant identification
Automatica (Journal of IFAC) - Special issue on trends in system identification
Parameter Estimation in the Presence of Bounded Data Uncertainties
SIAM Journal on Matrix Analysis and Applications
Robust Parameter Estimation in Computer Vision
SIAM Review
A tutorial history of least squares with applications to astronomy and geodesy
Journal of Computational and Applied Mathematics - Special issue on numerical analysis in the 20th century vol. 1: approximation theory
Least Square Estimation with Applications to Digital Signal Processing
Least Square Estimation with Applications to Digital Signal Processing
Modification of Method of Least Squares for Tutoring Neural Networks
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A robust high-order recursive quadratic algorithm for linear-in-the-parameters models
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
A high-order recursive quadratic learning algorithm
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Paper: Bounded-error parameter estimation: Noise models and recursive algorithms
Automatica (Journal of IFAC)
The min-max function differentiation and training of fuzzy neural networks
IEEE Transactions on Neural Networks
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A new algorithm called Mixed L2-Linfty (ML2) estimation is proposed in this paper; it combines both the weighted least squares and the worst-case parameter estimations together as the cost function and strikes the right balance between them. A robust ML2 algorithm and a practical approximate robust ML2 algorithm are also developed under disturbance signals. The properties of the new robust ML2 algorithm are analyzed and the simulation results are given to show the convergence and the validity.