查詢結果分析
相關文獻
- 結合人工智慧與專家知識之智慧型水庫操作系統
- 遺傳演算法在發展股市投資專家知識規則之研究
- 智慧型水庫即時操作控制系統
- 遺傳演算法在財務預測之應用
- 遺傳演算法於專家系統中參數優選之研究
- Structural Optimization Using Genetic Algorithms with Fuzzy Rule-Based Systems
- 線性軸幅路網接駁系統最適整合區位、路線與排班模式之研究
- Genetic Algorithm Approach for Designing Fir Hilbert Transformers and Differentiators
- 以類神經網路與遺傳演算法解決系統可用度分派問題
- 自我調適的動態排程系統--限制排程、模糊理論和遺傳演算法的應用
頁籤選單縮合
題名 | 結合人工智慧與專家知識之智慧型水庫操作系統=Integrating AI with Expert Knowledge to Build Intelligent Reservoir Operation System |
---|---|
作者 | 張斐章; 張雅婷; 張麗秋; | 書刊名 | 農業工程學報 |
卷期 | 50:4 2004.12[民93.12] |
頁次 | 頁14-27 |
分類號 | 443.96 |
關鍵詞 | 人工智慧; 水庫操作; 遺傳演算法; 模糊規則庫; 調適性網路模糊推論系統; Reservoir operation; Artifical intelligent; Genetic algorithm; Adaptive network-based fuzzy inference system; Fuzzy rule base; |
語文 | 中文(Chinese) |
中文摘要 | 面對臺灣地區水資源時空分佈不均及日益不足等問題,如何在安全條件下進行水庫操作使其儘可能滿足各標的,以善用水資源、維持環境永續性是當前首要課題。本研究以新穎的人工智慧相關理論,並結合現行規線操作的專家知識提出智慧型水庫操作策略,以石門水庫過去36年之水文狀況為例,進行實務模擬測試;首先利用遺傳演算法(GA)尋求歷史流量之水庫最佳放水量歷程,以茲作為調適性網路模糊推論系統(ANFIS)之訓練樣本與標的。為增加系統操作規則庫之完整性與合法性,乃研議水庫操作規線與模糊規則庫之間的轉換方法與機制,將操作規線所代表之蓄放標準轉換為規則(if-then)形式,建構出模糊規則知識庫,成功的將水庫傳統的操作策略與智慧型操作模式進行結合,藉由加入傳統操作方式的專家知識使系統更具『智慧』地處理資料與判斷資訊,進而有效地控制水庫水位與其放流量,提供水庫管理單至於蓄水利用運轉時有所參考及依據,測試結果顯示本研究所發展的模式較傳統規線操作方式在各項檢測指標上皆有大幅的改善,亦印證了模式的合理性與適切性。 |
英文摘要 | Resulting from the continuous increase in water demand and uneven water distribution both on time and space, the efforts, of pursuing integrated optimal water resource management become critical. In this study, we propose a novel intelligent control methodology that includes the genetic algorithm (GA), fuzzy rule base (FRB), and the adaptive network-based fuzzy inference system (ANFIS) to enhance the efficiency of reservoir operation. The Shihmen reservoir in north Taiwan is used as a case study, and its last thirty-six years hydrological data are used to train and/or verify the models’ performance. GA and FRB are used to extract the knowledge based on the historical inflow data with a design objective function and the traditional rule curve operating strategy, respectively. The ANFIS is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. The practicability and effectiveness of the proposed approach is tested on the operation of the Shihmen reservoir. The results show that the ANFIS models built on different types of knowledge have better performance than the traditional M-5 rule curves in reservoir operation. Moreover, we demonstrate that the ANFIS model can be more intelligent for reservoir operation if more information (or knowledge) is involved. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。