scholarly journals Theoretical and computational comparison of continuous-time process scheduling models for adjustable robust optimization

AIChE Journal ◽  
2018 ◽  
Vol 64 (8) ◽  
pp. 3055-3070 ◽  
Author(s):  
Nikolaos H. Lappas ◽  
Chrysanthos E. Gounaris
Keyword(s):  
Robust Optimization ◽  
Continuous Time ◽  
Time Process ◽  
Process Scheduling ◽  
Continuous Time Process ◽  
Adjustable Robust Optimization ◽  
Scheduling Models ◽  
Computational Comparison

A simple continuous-time process scheduling formulation and a novel solution algorithm

1996 ◽  
Vol 20 ◽  
pp. S1221-S1226 ◽  
Author(s):  
G. Schilling ◽  
C.C. Pantelides
Keyword(s):  
Continuous Time ◽  
Solution Algorithm ◽  
Time Process ◽  
Process Scheduling ◽  
Continuous Time Process

Parameter-Adaptive Control Based on Continuous-Time Process Models

1990 ◽  
Vol 23 (8) ◽  
pp. 443-448
Author(s):  
K. Peter ◽  
R. Isermann
Keyword(s):  
Adaptive Control ◽  
Continuous Time ◽  
Process Models ◽  
Time Process ◽  
Continuous Time Process ◽  
Parameter Adaptive

Stochastic ordering of random processes with an imbedded point process

1991 ◽  
Vol 28 (3) ◽  
pp. 553-567 ◽  
Author(s):  
François Baccelli
Keyword(s):  
Point Process ◽  
Continuous Time ◽  
Random Sequence ◽  
Stochastic Ordering ◽  
Random Processes ◽  
Time Process ◽  
Continuous Time Process ◽  
Partial Orderings ◽  
Stationary Probabilities

We introduce multivariate partial orderings related with the Palm and time-stationary probabilities of a point process. Using these orderings, we give conditions for the monotonicity of a random sequence, with respect to some integral stochastic ordering, to be inherited with a continuous time process in which this sequence is imbedded. This type of inheritance is also discussed for the property of association.

scholarly journals Mixed Spectra for Stable Signals from Discrete Observations

2021 ◽  
Vol 12 (05) ◽  
pp. 21-44
Author(s):  
Rachid Sabre
Keyword(s):  
Spectral Density ◽  
Continuous Time ◽  
Continuous Measurement ◽  
Spectral Measurement ◽  
Polynomial Kernel ◽  
Time Process ◽  
Discrete Observations ◽  
Continuous Part ◽  
Continuous Time Process ◽  
Discrete Measurement

This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.

scholarly journals How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise

2006 ◽  
pp. 3-72 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Per A. Mykland ◽  
Lan Zhang
Keyword(s):  
Market Microstructure ◽  
Continuous Time ◽  
Time Process ◽  
Microstructure Noise ◽  
Market Microstructure Noise ◽  
Continuous Time Process
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