https://scholars.lib.ntu.edu.tw/handle/123456789/87351
標題: | Counterpropagation fuzzy-neural network for city flood control system | 作者: | Chang, Fi-John Chang, Kai-Yao Chang, Li-Chiu |
關鍵字: | Fuzzy-neural network;Rule-base control;Artificial intelligence;Flood;Pumping station operation | 公開日期: | 八月-2008 | 起(迄)頁: | 24-34 | 來源出版物: | Journal of Hydrology | 摘要: | 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. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/176336 | DOI: | 10.1016/j.jhydrol.2008.05.013 |
顯示於: | 生物環境系統工程學系 |
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