淡江大學機構典藏:Item 987654321/44542
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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/44542


    Title: Counterpropagation Fuzzy-Neural Network for City Flood Control System
    Authors: Chang, Fi-John;Chang, Kai-yao;張麗秋;Chang, Li-chiu
    Contributors: 淡江大學水資源與環境工程學系
    Keywords: Fuzzy-neural network;Rule-base control;Artificial intelligence;Flood;Pumping station operation
    Date: 2008-08
    Issue Date: 2010-08-10 11:24:55 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: The counterpropagation fuzzy-neural network (CFNN) can effectively solve highly non-linear control problems and robustly tune the complicated conversion of human intelligence to logical operating system. We propose the CFNN for extracting flood control knowledge in the form of fuzzy if–then rules to simulate a human-like operating strategy in a city flood control system through storm events. The Yu-Cheng pumping station, Taipei City, is used as a case study, where storm and operating records are used to train and verify the model’s performance. Historical records contain information of rainfall amounts, inner water levels, and pump and gate operating records in torrential rain events. Input information can be classified according to its similarity and mapped into the hidden layer to form precedent if–then rules, while the output layer gradually adjusts the linked weights to obtain the optimal operating result. A model with increasing historical data can automatically increase rules and thus enhance its predicting ability. The results indicate the network has a simple basic structure with efficient learning ability to construct a human-like operating strategy and has the potential ability to automatically operating the flood control system.
    Relation: Journal of Hydrology 358(1-2), pp.24-34
    DOI: 10.1016/j.jhydrol.2008.05.013
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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