POLYNOMIAL SOLUTIONS FOR THE KOLMOGOROV-WIENER FILTER WEIGHT FUNCTION FOR FRACTAL PROCESSES - Gorev, V. N.; Gusev, A. Yu.; Korniienko, V. I. (Radio Electronics, Computer Science, Control, 2019)
Context. We consider a Kolmogorov-Wiener filter for fractal random processes, which, for example, may take place in modern information-telecommunication systems and in control of complex technological processes. The weight function of the considered filter may be applied to data forecast in the corresponding systems. Objective. As is known, in the continuous case the Kolmogorov-Wiener filter weight function obeys the Fredholm integral equation of the first kind. The aim of the work is to obtain the Kolmogorov-Wiener filter weight function as an approximate solution of the corresponding integral equation. Method. The truncated orthogonal polynomial expansion method for approximate solution of the Fredholm integral equation of the first kind is used. A set of orthonormal polynomials is used. Results. We obtained approximate results for the Kolmogorov-Wiener weight function for fractal processes with a power-law structure function. The weight function is found as an approximate solution of the Fredholm integral equation of the first kind the kernel of which is the correlation function of the corresponding fractal random process. Analytical results for the one-, two-, three-, four- and five-polynomial approximations are obtained. A numerical comparison of the left-hand and right-hand sides of the integral equation for the obtained weight functions is given for different values of the parameters. The corresponding numerical investigation is made up to the 18-polynomial approximation on the basis of the Wolfram Mathematica package. The applicability of the obtained solutions is discussed. Conclusions. The Kolmogorov-Wiener weight function for fractal processes is obtained approximately in the form of a truncated orthogonal polynomial series. The validity of the obtained weight functions is discussed. The obtained results may be applied to the data forecast in a wide variety of different systems where fractal random processes take place.