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Least-Squares Estimation
Least-squares estimation provides a means of determining estimates of model parameters that are optimal in the sense of minimizing the sum of the...
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Weighted Tensor Least Angle Regression for Solving Sparse Weighted Multilinear Least Squares Problems
Sparse weighted multilinear least-squares is a generalization of the sparse multilinear least-squares problem, where prior information about, e.g.,...
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Position Estimator for a Follow Line Robot: Comparison of Least Squares and Machine Learning Approaches
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of...
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Least Squares and Related
This chapter begins with a review of least squares and Procrustes problems and continues with a discussion of least squares in the linear separable...
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The posterior selection method for hyperparameters in regularized least squares method
The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods...
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Chaos Game Optimization-Least Squares Algorithm for Photovoltaic Parameter Estimation
Estimating the parameters of photovoltaic (PV) models accurately is vital to increase the effectiveness of PV systems. During the past few years,...
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Signal Frequency Estimation via Kalman Filter and Least Squares Approach for Non-uniform Signals
An innovative approach for estimating power system characteristics has been devised, and it is predicated on the Kalman filter (KF) and least squares...
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On Weighted Least Squares Estimators of Parameters of a Chirp Model
The least squares method seems to be a natural choice in estimating the parameters of a chirp model. But the least squares estimators are very...
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The Optimal Regularized Weighted Least-Squares Method for Impulse Response Estimation
The system identification literature has been going through a recent paradigm change with the emergent use of regularization and kernel-based...
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Iterative Parameter Identification for Time-delay Nonlinear Rational Models via L1-regularized Least Squares
This paper develops an unbiased iterative parameter identification algorithm for time-delay nonlinear rational systems. In order to reduce redundant...
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Uncalibrated Visual Servoing Using Dynamic Broyden and Least-Squares Methods
Visual servo systems usually require a calibration operation before performing their tasks. This involves extra time cost, and the calibration...
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Fuzzy Transform and Least-Squares Fuzzy Transform: Comparison and Application
Fuzzy transform is a novel and well-founded soft computing method for reconstruction and denoising of image data. Recently, a least-squares fuzzy...
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An adaptive identification method for outliers in dam deformation monitoring data based on Bayesian model selection and least trimmed squares estimation
An important technique for the quantitative analysis of dam deformation state is to establish safety monitoring models using deformation monitoring...
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Least Mean Squares Based Kalman Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems
Millimeter wave massive multiple-input and multiple-output (MIMO) systems support several antennas at the receiver and transmitter sides to serve...
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A Robust Constrained Total Least Squares Algorithm for Three-Dimensional Target Localization with Hybrid TDOA–AOA Measurements
Three-dimensional (3D) target localization by using hybrid time difference of arrival (TDOA) and angle of arrival (AOA) measurements from multiple...
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State Estimation in Electric Power Systems Using Weighted Least Squares Method
State estimation is a powerful method used in electric power systems, whose results are used for various purposes such as analysis, management and...
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Estimating Unknown Parameters of a Noisy Damped Real/Complex Sinusoidal Signal in Two Dimensions Based on the Integral Linear Least Squares Algorithm
In this article, a novel algorithm called integral linear least squares is proposed to estimate unknown parameters of a noisy damped complex/real...
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Performance assessment of Kriging with partial least squares for high-dimensional uncertainty and sensitivity analysis
This paper aims to assess the potential of Kriging combined with partial least squares (KPLS) for fast uncertainty quantification and sensitivity...
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Non-linear Weighted Least Squares Cooperative Localization Based on Multi Radar/Infrared
For the problems of passive localization of targets by multiple azimuths only between infrared sensors and large detection errors of radar sensors,...
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Convergence Analysis of Forgetting Factor Least Squares Algorithm for ARMAX Time-Delay Models
In this paper, the estimation problem is considered for both sample delay and coefficients of ARMAX model. An extended recursive least squares...
