https://repository.up.ac.za/bitstream/handle/2263/16429/Zhang_Modeling(2011).pdf?sequence.pdf

Modeling and energy efficiency optimization of belt conveyors

http://home.eng.iastate.edu/~jdm/wesep594/SauderPaper1.pdf

A new technique for identification of eighteen flutter derivatives using a three-degree-of-freedom section model

This paper presents an approach to time series analysis and ARMA spectral estimation from only the output data corrupted by noise. It is shown that the generalized (not well-known) modified Yule-Walker (MYW) equations hold when the residual is some correlated noise. To solve such equations, a new version of the generalized least squares (GLS) method is proposed, yielding AR parameter estimates with higher accuracy. This GLS method can also be used to enhance the estimation accuracy of AR parameters for short and noisy data in case of the MYW equation holding theoretically. Furthermore, a simple procedure for improving MA parameter estimates is studied. Our approach, as Cadzow's singular value decomposition (SVD) method, has provided significantly higher performance spectral estimates for a low-order ARMA model than those obtained via usual techniques which are based upon direct solution of the MYW equations and which use a high-order model without considering an additive noise.

An approach to time series analysis and ARMA spectral estimation

Brief paper: Fast GLS algorithm for parameter estimation

This paper presents a unified approach for the identification of linear state-space models from input-output measurements in the presence of noise. It is based on the established Observer/Kalman filter IDentification (OKID) method of which it proposes a new formulation capable of transforming a stochastic identification problem into a (simpler) deterministic problem, where the Kalman filter corresponding to the unknown system and the unknown noise covariances is identified. The system matrices are then recovered from the identified Kalman filter. The Kalman filter can be identified with any deterministic identification method for linear state-space models, giving rise to numerous new algorithms and establishing the Kalman filter as the unifying bridge from stochastic to deterministic problems in system identification.

/pdf/okid-as-a-unified-approach-to-system-identification-reaqe5oich.pdf

OKID as a Unified Approach to System Identification

A new least squares solntion for obtaining asymptotically unbiased and consistent estimates of unknown parameters in noisy linear systems is presented. The proposed algorithms are in many ways more advantageous than generalized least squares algorithm. Extensions to on-line and multivariable problems can be easily implemented. Examples are given to illustrate the performance of these new algorithms.

H. Kwakemaak

Papers

On Least Squares Algorithms for System Parameter Identification