Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

Abstract

Scientific and methodological foundations have been developed for the optimal identification of non-stationary objects transmitted by random time series in control systems of industrial and technological complexes, monitoring of environmental protection and ecology, palynology, medicine based on the use of statistical, dynamic, neural network models and mechanisms for extracting statistical, dynamic specific characteristics of information. The mechanisms for controlling the error in the identification of RTS, based on the use of statistical criteria - the rules of the sequential analysis, Bayesian estimation, ±3σ, confidence interval, have been investigated. The problem of control of the error in the identification of RTS by the confidence interval, the mechanisms of threshold control of the values of the random time series elements, their increments, the error of prediction by statistical, trend, dynamic models, algebraic polynomials, parabolic, cubic interpolation, extrapolation spline functions. Mechanisms for regulating the variables of the random time series identification models based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed. The methodology for assessing the quality of the identification of RTS by mathematical expectation and variance and the criterion of correspondence between the relations of mathematical expectation and variance of two consecutive elements have been implemented. The experimental study was carried out according to the real data of the power grid enterprise. The effectiveness of the implemented mechanisms was studied according to the criteria of the minimum root-mean-square error, labor intensity, cost of information processing.