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Found 10 papers, 0 papers with code
Adaptive Kalman Filtering Developed from Recursive Least Squares Forgetting Algorithms
KFLS is an extension of generalized forgetting recursive least squares (GF-RLS), a general framework which contains various extensions of RLS from the literature as special cases.
Efficient Batch and Recursive Least Squares for Matrix Parameter Estimation with Application to Adaptive MPC
Traditionally, batch least squares (BLS) and recursive least squares (RLS) are used for identification of a vector of parameters that form a linear model.
Convergence of Recursive Least Squares Based Input/Output System Identification with Model Order Mismatch
Next, this work analyzes online identification of input/output models in the case where the order of the identified model is higher than that of the true system.
SIFt-RLS: Subspace of Information Forgetting Recursive Least Squares
This paper presents subspace of information forgetting recursive least squares (SIFt-RLS), a directional forgetting algorithm which, at each step, forgets only in row space of the regressor matrix, or the \textit{information subspace}.
Adaptive Real-Time Numerical Differentiation with Variable-Rate Forgetting and Exponential Resetting
Digital PID control requires a differencing operation to implement the D gain.
Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees
This work present generalized forgetting recursive least squares (GF-RLS), a generalization of recursive least squares (RLS) that encompasses many extensions of RLS as special cases.
Nonlinear Trajectory-Based Region of Attraction Estimation for Aircraft Dynamics Analysis
A piecewise polynomial model of the GTM is used to simulate trajectories and the developed analysis tools are used to estimate the ROA around a trim condition based only on this trajectory data.
Regularization-Induced Bias and Consistency in Recursive Least Squares
Within the context of recursive least squares (RLS) parameter estimation, the goal of the present paper is to study the effect of regularization-induced bias on the transient and asymptotic accuracy of the parameter estimates.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
Then, it uses the segments to learn object models that can be used for detection in a static image.
ErrorNet: Learning error representations from limited data to improve vascular segmentation
In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.
