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Third-order cumulants based methods for continuous-time errors-in-variables model identification
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DOI：10.1016/j.automatica.2007.07.010.png)
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Abstract
En 中文
In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to have a skewed probability density function, whereas the noises contaminating the data are assumed to be symmetrically distributed. The third-order cumulants of the input/output data are then (asymptotically) insensitive to the noises, that can be coloured and/or mutually correlated. Using this noise-cancellation property two computationally simple estimators are proposed. The usefulness of the proposed algorithms is assessed through a numerical simulation. (C) 2007 Elsevier Ltd. All rights reserved.
Keywords:
system identification
errors-in-variables
continuous-time systems
higher-order statistics
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