Talk:Discretization/Archive 1

Archive 1

Extra

It would be useful if this article also covered discritizing stochastic differential equations for sampling. — Preceding unsigned comment added by 82.211.86.2 (talk) 13:58, 17 August 2006 (UTC)

Absolutely! Please contribute if you are familiar with the subject. :-) --Fredrik Orderud 22:56, 17 August 2006 (UTC)

Minor ambiguity

There is minor ambiguity by using T for the sampling time as in Qd the matrix Transpose also uses the T symbol. — Preceding unsigned comment added by 164.11.204.52 (talk) 14:01, 18 October 2006 (UTC)

Tustin (bilinear)

Tustin(bilinear) is not an approximation but rather an exact transformation or mapping if you like... 24 March 2009 —Preceding unsigned comment added by 131.180.28.210 (talk) 15:20, 24 March 2009 (UTC)

Opposite of discretization

Is there a name for the opposite of discretization? "to make something consisting of discrete parts into a continuous entity"? —Preceding unsigned comment added by 71.67.248.185 (talk) 00:32, 28 June 2010 (UTC)

"continuous zero-mean white noise" is not realistic

In my opinion "continuous zero-mean white noise" is not realistic. A noise is white over a finite bandwidth otherwise noise energy goes to infinity.... --213.224.27.206 (talk) 09:43, 17 January 2012 (UTC)

Approximations section

Under the "Approximations" section, could someone (I'll maybe do it when I have some free time) please add the scaling-and-squaring method to approximate matrix exponentials? (for more info, see SIAM J. MATRIX ANAL. APPL. Vol. 26, No. 4, pp. 1179–1193, "The Scaling and Squaring method for the matrix exponential revisited.") XWolfRH (talk) 17:18, 10 April 2012 (UTC)

in the "Discretization of linear state space models" section

I was wondering about the statement "where v and w are continuous zero-mean white noise sources with covariances..." Everything that I have been reading (and I am relatively new to stochastic processes and Kalman filters) says that the covariances of continuous-time white Gaussian noise processes are infinite, stemming from the Dirac delta function in the definition of the autocorrelation function. This would apply to the covariance matrix as well. On the other hand, the power spectral density matrix is finite. My questions are: 1) are Q and R for the continuous-time noises really the PSD matrices and not covariance matrices? 2) If so, does this change the equations and/or procedure for finding the discrete-time version Qd in the "Discretization of process noise" section? Thanks, 68.83.8.55 (talk) 03:57, 26 September 2012 (UTC)

Discretization of a function

I can find no reliable sources that discuss the following as "discretization of a function", let alone that this is what it means "In mathematics". That seems like WP:OR to me. I would be perfectly willing to restore it, if it can be appropriately sourced, and "In mathematics" is replaced by whatever seems most appropriate ("in sources like XXX..." maybe).

In mathematics, the discretization of a function is the operation ${\bot \!\bot \!\bot }_{T}$ that assigns the generalized function ${\bot \!\bot \!\bot }_{T}f$ defined by

$({\bot \!\bot \!\bot }_{T}f)(t)\,{\stackrel {\mathrm {def} }{=}}\,\sum _{k=-\infty }^{\infty }\,f(kT)\,\delta (t-kT)$

to a smooth regular function $f(t)$ that is not growing faster than polynomials, where $\delta (t)$ is the Dirac delta and $T$ is a positive, real increment between consecutive samples $f(kT)$ of function $f(t)$. The generalized function ${\bot \!\bot \!\bot }_{T}f$ is also called the discretization of $f$ with increments $T$ or discrete function of $f$ with increments $T$. Discretization is an operation that is closely related to periodization via the Discretization-Periodization theorem. Example: Discretizing the function that is constantly one yields the Dirac comb.

Sławomir Biały (talk) 11:55, 16 May 2015 (UTC)