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A numerical solution to a 30-dimensional water reservoir network optimization problem, based on stochastic dynamic programming, is presented.
This article presents an alternative approach to solve hydrothermal systems operation planning based on stochastic dynamic programming.
However, the use of resolution algorithms based on stochastic dynamic programming, with their high computational requests, imposes strong simplification in modelling the water system.
A class of algorithms, based on Stochastic Dynamic Programming (SDP), solves the Control Problem with a Functional Approach, by posing very mild conditions to the characteristics of the problem.
Existing dynamic selection algorithms are either 'myopic' and consider only those patches that have already been protected, totally ignoring future uncertainty, or they are based on stochastic dynamic programming, which delivers the optimal strategy taking uncertainty into account but is numerically too complex to be employed in actual selection problems.
A systematic approach based on stochastic dynamic model is proposed for biologists to generate insight into the underlying defense mechanisms of endothelial inflammatory systems via the construction of corresponding signaling networks upon specific stimulus.
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KF technique makes predictions based on stochastic and dynamic structures.
Reliability analysis is based on stochastic discrete event models like stochastic Petri nets.
Our mechanism is based on stochastic datagram relocation.
The algorithm is developed based on stochastic finite-element method.
The solution method is based on stochastic successive linear programming.
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