Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling |
| |
Authors: | M Baglietto C Cervellera M Sanguineti R Zoppoli |
| |
Institution: | 1.Department of Communications, Computer and System Sciences (DIST),University of Genoa,Genoa,Italy;2.Institute of Intelligent Systems for Automation (ISSIA-CNR),National Research Council of Italy, Genoa Branch,Genoa,Italy |
| |
Abstract: | Two methods of approximate solution are developed for T-stage stochastic optimal control (SOC) problems, aimed at obtaining finite-horizon management policies for water resource
systems. The presence of uncertainties, such as river and rain inflows, is considered. Both approaches are based on the use
of families of nonlinear functions, called “one-hidden-layer networks” (OHL networks), made up of linear combinations of simple
basis functions containing parameters to be optimized. The first method exploits OHL networks to obtain an accurate approximation
of the cost-to-go functions in the dynamic programming procedure for SOC problems. The approximation capabilities of OHL networks
are combined with the properties of deterministic sampling techniques aimed at obtaining uniform samplings of high-dimensional
domains. In the second method, admissible solutions to SOC problems are constrained to take on the form of OHL networks, whose
parameters are determined in such a way to minimize the cost functional associated with SOC problems. Exploiting these tools,
the two methods are able to cope with the so-called “curse of dimensionality,” which strongly limits the applicability of
existing techniques to high-dimensional water resources management in the presence of uncertainties. The theoretical bases
of the two approaches are investigated. Simulation results show that the proposed methods are effective for water resource
systems of high dimension. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|