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 4, Issues 1-2 (1993), pp. 94–110
Abstract
In the previous paper (Pupeikis, 1992) the problem of off-line estimation of dynamic systems parameters in the presence of outliers in observations have been considered, when the filter generating an additive noise has a very special form. The aim of the given paper is the development, in such a case, of classical generalized least squares method (GLSM) algorithms for off-line estimation of unknown parameters of dynamic systems. Two approaches using batch processing of the stored data are worked out. The first approach is based on the application of S-, H-, W- algorithms used for calculation of M-estimates, and the second one rests on the replacement of the corresponding values of the sample covariance and cross-covariance functions by their robust analogues in respective matrices of GLSM and on a further application of the least squares (LS) parameter estimation algorithms. The results of numerical simulation by IBM PC/AT (Table 1) are given.
Journal:Informatica
Volume 3, Issue 4 (1992), pp. 567–581
Abstract
In the previous papers (Pupeikis, 1990; 1991; 1992) the problems of model oder determination and recursive estimation of dynamic systems parameters in the presence of outliers in observations have been considered. The aim of the given paper is the development, in such a case, of classical off-line algorithms for systems of unknown parameters estimation using batch processing of the stored data. An approach, based on a substitution of the corresponding values of the sample covariance and cross-covariance functions by their robust analogues in respective matrices and on a further application of the least square (LS) parameter estimation algorithm, is worked out. The results of numerical simulation by IBM PC/AT (Table 1, 2) are given.
Journal:Informatica
Volume 3, Issue 1 (1992), pp. 88–97
Abstract
In the previous paper (Pupeikis, 1990) the problem of model order determination in the presence of outliers in observations has been considered by means of introducing robust analogues of the sample covariance and cross-covariance functions instead of the respective classical function meanings used in the determinant ratio test. The aim of the given paper is the development of statistical hypothesis-testing procedures for determination of the model order of dynamic objects, described by linear difference equations. The results of numerical simulations by computer (Table 1) show the efficiency of the proposed statistical procedures for determining the model order by input-output data in the presence of outliers in observations.
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.