On a least-squares-based algorithm for identification of stochasticlinear systems

Authors:
Wei Zheng
Affiliations:
Dept. of Math., Univ. of Western Sydney, Sydney, NSW
Venue:
IEEE Transactions on Signal Processing
Year:
1998

Citing 0
Cited 8

Bilinear equation method for unbiased identification of linear FIR systems in the presence of input and output noises

Signal Processing
Multi-innovation stochastic gradient algorithm for output error systems based on the auxiliary model

ACC'09 Proceedings of the 2009 conference on American Control Conference
A fast algorithm for AR parameter estimation using a novel noise-constrained least-squares method

Neural Networks
Gradient based and least-squares based iterative identification methods for OE and OEMA systems

Digital Signal Processing
Brief On the relation between a bias-eliminated least-squares (BELS) and an IV estimator in closed-loop identification

Automatica (Journal of IFAC)
Robust identification of continuous systems with dead-time from step responses

Automatica (Journal of IFAC)
Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods

Mathematical and Computer Modelling: An International Journal
Comparing some classes of bias-compensating least squares methods

Automatica (Journal of IFAC)

Quantified Score

Hi-index	35.69

Visualization

Abstract

A new form of bias-eliminated least-squares (BELS) algorithm is developed to identify transfer function parameters of a linear time-invariant system, irrespective of noise dynamics. Unlike the BELS estimator previously presented, the main feature with the developed algorithm is that the transfer function parameters are consistently estimated in such a direct way that there is no need to prefilter observed data or to deal with a high-order augmented system. This greatly simplifies implementation of the BELS-based algorithms and reduces numerical efforts, whereas a desirable estimation accuracy can still be achieved. Two simulation examples are presented that clearly illustrate the good performances of the developed algorithm, including its superiority over one type of simple instrumental variable method