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This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary (reference) model approach, we present a recursive least squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the...
In this paper, we focus on a class of dual-rate sampled-data systems in which all the inputs u(t) are available at each instant while only scarce outputs y(qt) can be measured (q > 1 being an integer). To estimate the parameters of the dual-rate systems, we derive a mathematical model by using the polynomial transformation technique, and apply the extended least squares algorithm to identify the...
This paper derives an identification model for a class of stochastic systems with colored noises. The information vector in the identification model contains both unknown noise-free outputs (i.e., true outputs) and unmeasurable noise terms, this is difficulty of identification. This paper establishes an auxiliary model by using the measurable information of the system and replaces the unknown noise-free...
This paper focuses on identification problems of auto-regression (AR) models with missing output observation data. The standard least squares algorithm cannot be applied to the AR models due to the missing output data. To estimate the parameters of the AR models, we employ the polynomial transformation technique to transform the AR models into the auto-regression moving average (ARMA) models, which...
In this paper, we combine the hierarchical identification principle with the least square algorithm to identify the parameters of dual-rate sampled-data systems. The hierarchical identification principle is to decompose the identification model of dual-rate systems to several identification sub-models with smaller dimensions and fewer parameters to be estimated, and to present the hierarchical least...
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