adaptive stochastic control
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2020 ◽  
Vol 63 ◽  
pp. 102473
Author(s):  
Iordanis K. Giannopoulos ◽  
Assimakis K. Leros ◽  
Apostolos P. Leros ◽  
Georgios Tsaramirsis ◽  
Madini O. Alassafi

2019 ◽  
Vol 67 (4) ◽  
pp. 314-321
Author(s):  
Tomas Kozel ◽  
Milos Stary

Abstract The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.


2011 ◽  
Vol 99 (6) ◽  
pp. 1098-1115 ◽  
Author(s):  
Roger N. Anderson ◽  
Albert Boulanger ◽  
Warren B. Powell ◽  
Warren Scott

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