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- Title
Simulation with RBF Neural Network Model for Reservoir Operation Rules.
- Authors
Yi-min Wang; Jian-xia Chang; Qiang Huang
- Abstract
Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particle swarm optimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study.
- Subjects
RESERVOIRS; FLOOD control research; WATER supply management; WATER power; SIMULATION methods &; models; STATISTICS; ALGORITHMS; PARTICLE swarm optimization
- Publication
Water Resources Management, 2010, Vol 24, Issue 11, p2597
- ISSN
0920-4741
- Publication type
Article
- DOI
10.1007/s11269-009-9569-0