Counterpropagation Fuzzy-Neural Network for City Flood Control System
學年 97
學期 1
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 en
ISSN 0022-1694
期刊性質
收錄於 SCI EI
產學合作
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國別 NLD
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