By Topic

Algorithmic modeling of TES processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. R. Jelenkovic ; Dept. of Electr. Eng., Columbia Univ., New York, NY, USA ; B. Melamed

TES (transform-expand-sample) is a versatile class of stationary stochastic processes which can model arbitrary marginals, a wide variety of autocorrelation functions, and a broad range of sample path behaviors. TES parameters are of two kinds: the first kind is used for the exact fitting of the empirical distribution (histogram), while the second kind is used for approximating the empirical autocorrelation function. Parameters of the first kind are easy to determine algorithmically, but those of the second kind require a hard heuristic search on a large parametric function space. This paper describes an algorithmic procedure which can replace the heuristic search, thereby largely automating TES modeling. The algorithm is cast in nonlinear programming setting with the objective of minimizing a weighted sum of squared differences between the empirical autocorrelations and their candidate TES model counterparts. It combines a brute-forte search with steepest-descent nonlinear programming using Zoutendijk's feasible direction method. Finally, we illustrate the efficacy of our approach via three examples: two from the domain of VBR (variable bit rate) compressed video and one representing results from a laser intensity experiment

Published in:

IEEE Transactions on Automatic Control  (Volume:40 ,  Issue: 7 )