An off-line identification method based on the least-squares approximation technique is applied for identifying of electromechanical parameters of a servo drive model. The proposed identification method solves a problem of experimental data automatic obtaining with angular velocity limitation and angle limitation. The developed algorithm can be automatically adjusted during its work to achieve ...

Parametric identification requires a good know-how and an accurate analysis. The most popular methods consist in using simply the least squares techniques because of their simplicity. However, these techniques are not intrinsically robust. An alternative consists in helping them with an appropriate data treatment. Another choice consists in applying a robust identification method. This paper fo...

Al~trad-The least squares parametric system identification algorithm is analyzed assuming that the noise is a bounded signal. A bound on the worst-case parameter estimation error is derived. This bound shows that the worst-case parameter estimation error decreases to zero as the bound on the noise is decreased to zero. 1. Introduction THE LEAST SQUARES ALGORITHM, due to Gauss, is one of the mos...

Design and modeling of nonlinear systems require the knowledge of all inside acting parameters and effects. An empirical alternative is to identify the system’s transfer function from input and output data as a black box model. This paper presents a procedure using least squares algorithm for the identification of a feed drive system coefficients in time domain using a reduced model based on wi...

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Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher-order statistical moments in nonGaussian noise environments. Although correntropy has been used with complex data, no theoretical study was pursued to elucidate its properties, nor how to best use it for optimization . This paper presents a probabilistic interpretation for correntropy using complex-value...

In this paper, we propose a new recursive subspace model identification (RSMI) based on regression and natural power method (NP) which is an array signal processing algorithm with excellent convergence properties. We call this new algorithm as ‘R-NP’. The basic idea of the algorithm is to utilize an unstructured least squares linear regression approach at the updating observation vector step an...