This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood with extended Kalman filter and maximum likelihood with unscented Kalman filter. We perform the identifi...

Initial dissertation research in the area of real-time system identification of ship hydrodynamic coefficients is presented. In accordance with the naval initiative of Seabasing requiring automation of small vessels, a real-time coefficient system identification method is being researched. Ship coefficient system identification has been relatively neglected with efforts limited only to Kalman f...

The paper presents a general framework for the Frisch scheme and the extended compensated least squares technique within which two new algorithms for the identification of single-input single-output linear time-invariant errors-in-variables models are proposed. The first algorithm is essentially the Frisch scheme using a novel model selection criterion. The second method is a modification of th...

Abstract: This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model base...

Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two rec...

For the lifted input–output representation of general dual-rate sampled-data systems, this paper presents a decomposition based recursive least squares (D-LS) identification algorithm using the hierarchical identification principle. Compared with the recursive least squares (RLS) algorithm, the proposed D-LS algorithmdoes not require computing the covariancematriceswith large sizes andmatrix in...

Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered (m-RLS) address concern. The m-RLS composed multiple estimators, each which employed and eliminate misadjustment previous l...