教師資料查詢 | 類別: 期刊論文 | 教師: 張麗秋 LI-CHIU CHANG (瀏覽個人網頁)

標題:Counterpropagation Fuzzy-Neural Network for City Flood Control System
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學期
出版(發表)日期2008/08/01
作品名稱Counterpropagation Fuzzy-Neural Network for City Flood Control System
作品名稱(其他語言)
著者Chang, Fi-John; Chang, Kai-yao;張麗秋; Chang, Li-chiu
單位淡江大學水資源與環境工程學系
出版者Amsterdam: Elsevier BV
著錄名稱、卷期、頁數Journal of Hydrology 358(1-2), pp.24-34
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.
關鍵字Fuzzy-neural network; Rule-base control; Artificial intelligence; Flood; Pumping station operation
語言英文
ISSN0022-1694
期刊性質
收錄於SCI;EI
產學合作
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國別荷蘭
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