Journal:Informatica
Volume 7, Issue 1 (1996), pp. 27–38
Abstract
In the papers (Kaminskas, 1973; Kaminskas and Nemura, 1975) the stopping rule of recursive least squares (RLS) is worked out using the length of the confidence interval for the respective current meaning of the true output signal of a linear dynamic system. The aim of the given paper is the development of techniques for calculating threshold intervals of respective criteria, used in such a stopping rule. In this connection adaptive threshold intervals based on the Cramer-Rao lower bound according to Pupeikis (1995) are proposed here, too. The results of numerical simulation by IBM PC/AT are given.
Journal:Informatica
Volume 6, Issue 3 (1995), pp. 299–312
Abstract
In the papers (Kaminskas, 1972; Kaminskas and Nemura, 1975; Yin, 1989) the stopping rules of recursive least squares (RLS) are worked out using the ellipsoidal confidence region for the respective parameter vector of a linear dynamic system. The aim of the given paper is the development of the technique for calculating threshold intervals of respective criterions, used in a stopping rule, which are presented in Kaminskas (1972). In this connection adaptive threshold intervals based on the Cramer-Rao lower bound are proposed here. The results of numerical simulation by IBM PC/AT are given.
Journal:Informatica
Volume 5, Issues 1-2 (1994), pp. 189–210
Abstract
In the previous papers (Novovičova, 1987; Pupeikis 1991) the problem of recursive least square (RLS) estimation of dynamic systems parameters in the presence of outliers in observations has been considered, when the filter, generating an additive noise, has a transfer function of a particular form, see Fig. 1, 2. The aim of the given paper is the development of well-known classical techniques for robust on-line estimation of unknown parameters of linear dynamic systems in the case of additive noises with different transfer functions. In this connection various ordinary recursive procedures, see Fig. 2–6, are worked out when systems' output is corrupted by the correlated noise containing outliers. The results of numerical simulation by IBM PC/AT (Table 1) are given.
Journal:Informatica
Volume 2, Issue 4 (1991), pp. 579–592
Abstract
In the previous paper (Pupeikis, 1990) the problem of model order determination in the presence of outliers in observations has been considered. The aim of the given paper is the development of the recursive algorithms of computation of M-estimates ensuring their stability conditions. In this connection the approach, based on adaptive Huber's monotone psi-function, is worked out. It is also used for the detection of the outliers in time series and for the correction both outliers and M-estimates during successive calculations. The results of numerical simulation by computer (Fig. 1 and Table 1) are given.