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- W2895956350 abstract "Abstract This paper proposes a method for the estimation of ARX (Autoregressive with external input) model of multivariable errors-in-variables (EIV) systems. In parameter estimation, the input noise variances need to be estimated in order to obtain a consistent estimate. Two methods are developed to estimate the input noises variances. One way is to minimize a correlated output error criterion. The other way is to extract the input noises variances from the power spectrum of measured inputs. When the input noise variances are known, a modified least-squares method will give consistent estimate for the EIV system. Numerical simulations are used to illustrate the performance of the method. This work lays down a basis for extending the asymptotic method of Liu and Zhu (2017) to multivariable EIV systems which starts from a high order ARX model estimation." @default.
- W2895956350 created "2018-10-26" @default.
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- W2895956350 date "2018-01-01" @default.
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- W2895956350 title "ARX Model Estimation of Multivariable Errors-in-Variables Systems" @default.
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- W2895956350 doi "https://doi.org/10.1016/j.ifacol.2018.09.111" @default.
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