System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
On the convergence of He's variational iteration method
Journal of Computational and Applied Mathematics
The residual based extended least squares identification method for dual-rate systems
Computers & Mathematics with Applications
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
Identification for multirate multi-input systems using the multi-innovation identification theory
Computers & Mathematics with Applications
Brief paper: Least squares based iterative identification for a class of multirate systems
Automatica (Journal of IFAC)
Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems
Computers & Mathematics with Applications
On a least-squares-based algorithm for identification of stochasticlinear systems
IEEE Transactions on Signal Processing
Mathematical and Computer Modelling: An International Journal
Observable state space realizations for multivariable systems
Computers & Mathematics with Applications
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This paper studies the modelling and identification problems for multi-input single-output (MISO) systems with colored noises. In order to obtain the unbiased recursive estimates of the systems, this paper presents a recursive least squares (RLS) identification algorithm based on bias compensation technique. The basic idea is to eliminate the estimation bias by adding a correction term in the least squares (LS) estimates, a set of stable digital prefilters are suitably designed to preprocess the input sampled data from multi-input channels for the purpose of getting the bias term arisen by colored noises in LS estimates, and further to derive a bias compensation based RLS algorithm. The performance of the developed method is both analyzed theoretically and shown by means of simulation results.