Brief Variable gain parameter estimation algorithms for fast tracking and smooth steady state

Authors:
Er-Wei Bai;Yih-Fang Huang
Affiliations:
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA;Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
Venue:
Automatica (Journal of IFAC)
Year:
2000

Citing 5
Cited 0

Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise

IEEE Transactions on Information Theory
Parameter bounding for time-varying systems

Mathematics and Computers in Simulation - Parameter identifications with error bound
Tracking characteristics of an OBE parameter-estimation algorithm

IEEE Transactions on Signal Processing
The error variance of LMS with time-varying weights

IEEE Transactions on Signal Processing
Paper: Bounded-error parameter estimation: Noise models and recursive algorithms

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.14

Visualization

Abstract

Based on the ideas of the set-membership identification, we present in this paper variable gain least mean-square (LMS) and weighted recursive least-squares (WRLS) algorithms. It is shown that the proposed algorithms possess a fast tracking ability to the variations of the parameter and at the same time are less sensitive to noise leading to a smooth steady state.