Fuzzy Neural Network Modeling of Reservoir Operation
Fuzzy Neural Network Modeling of Reservoir Operation
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DOI：
10.1061/(asce)0733-9496(2009)135:1(5
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发表时间：
2009
影响因子：
3.1
通讯作者：
P. Deka;V. Chandramouli
中科院分区：
文献类型：
--
作者：
P. Deka;V. Chandramouli
本研究旨在将人工神经网络和模糊逻辑组成的混合模型在关键时期的水库调度策略中的应用。所提出的混合模型[模糊神经网络（FNN）]结合了人工神经网络的学习能力和模糊逻辑的透明性。 FNN 模型在研究不同变量之间的非线性关系方面具有高度适应性和高效性。 FNN 模型的开发是为了研究印度阿萨姆邦 Pagladiya 河拟建水库的最佳释放运行政策的行为。这里，通过动态规划制定水库调度政策。最佳释放量与存储、流入和需求有关。通过案例研究讨论了使用 FNN 模型在水库释放中的优点。
The present study aims at the application of the hybrid model, which consists of artificial neural network and fuzzy logic in the reservoir operating policy during critical periods. The proposed hybrid model [fuzzy neural network (FNN)] combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The FNN model has been developed to study the behavior of optimal release operating policy on the proposed reservoir in Pagladiya River of the Assam State in India. Here, reservoir operation policies were formulated through dynamic programming. The optimal release was related to storage, inflow, and demand. The advantages of using the FNN model in reservoir release are discussed using the case study.