Adaptive and Minimax Methods of Prediction Dynamic Systems using the Kalman Algorithm
minimax, filtering, linear, extrapolation, stationary, saddle-point, disturbance, dispersion
In the article we consider the problem of linear extrapolation of zero-mean wide-sense-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
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