2001-01-012024-05-17https://scholars.lib.ntu.edu.tw/handle/123456789/684724摘要：本研究以調適性網路模糊推論系統(ANFIS)模擬控制水庫操作之放流量。ANFIS係將模糊邏輯理論架構在類神經網路系統上，以便於處理數值資料與定性的知識；此模式運用在水庫操作系統的主要步驟有二：(一)資料前處理:收集歷史流量資料與建立適當的水庫操作目標函數，套用遺傳演算法求得最佳放流歷程作為類神經網路的訓練標型；(二)建立ANFIS控制模式：從網路訓練時期調整適當的參數後，以推求未來時期水庫之最佳放流量；本研究以石門水庫操作為例，結合上述方法，由歷史流量模擬操作結果，並與傳統的石門水庫M-5規線操作結果相比較。<br> Abstract: By combining fuzzy inference systems and neural networks, ANFIS not only can handle both quantitative and qualitative knowledge, but also can successfully deal with the control laws for a complex system. Two main procedures are performed in order to implement the model to reservoir operation system. First, the genetic algorithm is used to search the optimal reservoir operating histogram which is recognized as the training pattern is the next step. Second, the ANFIS model is built to create the fuzzy inference system, to propose the suitable parameters, and to estimate the optimal water release. The proposed model is intended to investigate its practicability and efficiency by using the Shihmen reservoir. The M-5 rule curves are also performed for the purpose of comparison. The results show that the ANFIS model has better performance than the M-5 rule curves.水庫操作遺傳演算法類神經網路調適性網路模糊推論系統reservoir operationGenetic AlgorithmArtificial Neural NetworkAdaptive Network-based Fuzzy Inference System