Adaptive and Minimax Methods of Prediction Dynamic Systems using the Kalman Algorithm

Authors

  • Sidorov I.G.

minimax, filtering, linear, extrapolation, stationary, saddle-point, disturbance, dispersion

Abstract

In the article we consider the problem of linear extrapolation of zero-mean widesense-stationary random process both discrete-time and continuous-time cases under conditions of the absence of a priori information about the statistical characteristics of disturbance in the absence of measurement errors under scalar observation only the restricted disturbance is assumed. We investigate a minimax approach, which guarantees the prediction of high quality at the least favorable disturbance spectrum. The simple implementation of an optimal adaptive minimax predictor and prediction based on Kalman -Bucy filter and their comparative characteristics has been obtained. Examples are given.

Downloads

How to Cite

Adaptive and Minimax Methods of Prediction Dynamic Systems using the Kalman Algorithm. (2023). Global Journal of Science Frontier Research, 23(F1), 19-34. https://journalofscience.org/index.php/GJSFR/article/view/102610

References

Ulf Grenander (1963) A prediction problem in game theory. 3(4), 371-379.

M Moklayachuk, O Masyutka (2012) Estimation of Multidimensional Stationary Stochastic Sequences from Observations in Special Sets of Points. 249-308.

S Makridakis, S Wheelwright (1978) Forecasting Metods and Applications.

Jurgen Franke (1984) ON THE ROBUST PREDICTION AND INTERPOLATION OF TIME SERIES IN THE PRESENCE OF CORRELATED NOISE. 5(4), 227-244.

J Franke, H Poor (1984) Minimax robust filtering and finite -length robust predictors. 26(4), 87-126.

N Livshits, V Vinogradov, G Golubev (1974) Korrelyatsionnaya teoriya optimalnogo upravleniya mnogomernymi protsessami.

M Crane, A Nudelman (1973) The Čebyšev-Markov problem with moments in a parallelepiped. 207-235.

O Kurkin, Ju Korobochkin, S Shatalov (1990) Minimaksnaja obrabotka informacii [Minimax treatment of information.

O Kurkin (1981) Minimax linear filtration of a stationary random process with the restricted disturbancevariance//Radiotechnique and electronics. 26, 1689-1696.

O Kurkin (2001) Guaranteed Estimation Algorithms for Prediction and Interpolation of Random Processes. 62(4), 568-579.

Korobochkin Yu (1983) Minimax linear estimation of a stationary occasional sequence in the presence of disturbance with restricted dispersion//Radio technique and electronics. 26, 2186-2190.

I Sidorov (2018) Linear Minimax Filtering of a Stationary Random Process under the Condition of the Interval Fuzziness in the State Matrix of the System with a Restricted Variance. 63(8), 902-907.

M Athens, P Falb (1968) Optimal control. M.

A Fedotov (1990) Incorrect problems with occasional mistakes in the basic data.

E Sage, J Mels (1976) Communication Privacy Management Theory.

A Albert (1977) Regression, pseudo inversion and recurrent evaluation.

Adaptive and Minimax Methods of Prediction Dynamic Systems using the Kalman Algorithm

Published

2023-03-03

How to Cite

Adaptive and Minimax Methods of Prediction Dynamic Systems using the Kalman Algorithm. (2023). Global Journal of Science Frontier Research, 23(F1), 19-34. https://journalofscience.org/index.php/GJSFR/article/view/102610