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Τι (ποιος) είναι Wiener filtering - ορισμός
FILTER TO REDUCE THE AMOUNT OF NOISE PRESENT IN A SIGNAL
Weiner filter; Kolmogorov-Wiener filter; Wienerov filter; Wiener filtering
Wiener filter
In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process.
Wiener series
ORTHOGONAL EXPANSION FOR NONLINEAR FUNCTIONALS CLOSELY RELATED TO THE VOLTERRA SERIES AND HAVING THE SAME RELATION TO IT AS AN ORTHOGONAL HERMITE POLYNOMIAL EXPANSION HAS TO A POWER SERIES
Wiener kernel
In mathematics, the Wiener series, or Wiener G-functional expansion, originates from the 1958 book of Norbert Wiener. It is an orthogonal expansion for nonlinear functionals closely related to the Volterra series and having the same relation to it as an orthogonal Hermite polynomial expansion has to a power series.
Wiener process
![2}} times the [[Laplace–Beltrami operator]]. The image above is of the Brownian motion on a special manifold: the surface of a sphere.](https://commons.wikimedia.org/wiki/Special:FilePath/BMonSphere.jpg?width=200 "2}} times the [[Laplace–Beltrami operator]]. The image above is of the Brownian motion on a special manifold: the surface of a sphere.")
STOCHASTIC PROCESS GENERALIZING BROWNIAN MOTION
Wiener Process; Wiener measure; Wiener integral; Standard Brownian motion; Real Brownian motion; Integrated Brownian motion; Integrated Wiener process; Wiener process with drift; Brownian walk
In mathematics, the Wiener process is a real-valued continuous-time stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the one-dimensional Brownian motion.N.
Βικιπαίδεια
Wiener filter
In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process.
