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論文名稱 Title |
使用SDM-PRN轉換法以輔助建構系統動力學模型及政策設計 The Use of SDM-PRN Transformation for System Dynamics Model Construction and Policies Design |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
128 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2000-06-21 |
繳交日期 Date of Submission |
2001-06-29 |
關鍵字 Keywords |
類神經網路、政策設計、建模過程、機器學習、系統動力學 System Dynamics, Machine Learning, Policy Design, Model Construction, Neural Network |
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統計 Statistics |
本論文已被瀏覽 5845 次,被下載 2838 次 The thesis/dissertation has been browsed 5845 times, has been downloaded 2838 times. |
中文摘要 |
本研究的目的是要提出一個系統動力模型(System Dynamics Model;以下簡稱SDM)與類神經網路(Artificial Neural Network;以下簡稱ANN)之間的轉換法,用以輔助建構SDM並設計其中的政策。SDM與ANN都是將建模者的知識儲存在圖形的結構之中。ANN又能夠從一組多變量的時間序列軌跡中學習出一組數值的傳遞結構。因此我們將先把SDM轉換成一種特殊的ANN-部分遞迴網路(Partial Recurrent Network;以下簡稱PRN),並證明兩者具有相同的數值傳遞限制。再將PRN的學習法與建模過程整合,而形成一套學習機制,便可以輔助建模者建構SDM。也就是由模型的草圖開始,由PRN學習出幾個可能的結構,再由建模者選擇。另外,以同樣的精神,也可以將SDM-PRN轉換法應用來設計SDM中的政策。因為PRN可以從歷史軌跡學習出結構,當然也可以從建模者設定的較佳軌跡中,學習出較佳的結構。本研究也實證了上述兩個應用的有效性及使用性,結果都非常令人滿意。 |
Abstract |
This paper presents a model transformation between System Dynamics Model (SDM) and Artificial Neural Network (ANN) to aid model construction and policy design. We first point out a similarity between a System Dynamics Model (SDM) and an artificial neural network, in which both store knowledge majorly in the structure (or linkages) of a model. Then, we design a method that can map a SDM to a special design Partial Recurrent Network (PRN), and prove in mathematics that they two operate under the same numerical propagation constraints. With the established foundation, we then showed that the SDM-PRN transformation could aid SDM construction in the following way: (1) start from an initial skeleton of a PRN model (mapping from an initial SDM), (2) incarnate its structure by learning and (3) convert it back to a corresponding SDM. This approach integrates the capability of neural network learning with a traditional process, which thus makes model construction more systematic and much easier for common people. In the same philosophy, the SDM-PRN transformation could also aid SD policy design. Since any PRN can learn some structures from a historical time series pattern, it can also learn a better structure from a better pattern set by designer. We have investigated the effectiveness and usefulness of two application of the SDM-PRN transformation described above and the results are satisfactory. |
目次 Table of Contents |
摘要 I ABSTRACT II 目錄 III 表目錄 V 圖目錄 VI 第一章 導論 1 第一節 研究背景 1 第二節 研究動機及問題 1 第三節 研究目的 3 第四節 論文架構 4 第二章 文獻探討 5 第一節 系統動力學回顧 5 第二節 系統動力建模程序回顧 9 第三節 正規化政策設計方法回顧 15 第四節 類神經網路回顧 21 第三章 SDM-PRN模型轉換法 29 第一節 結構之對應 29 第二節 數值限制式之對應 41 第三節 實證 50 第四章 模型轉換法應用之一:輔助建模 55 第一節 問題之定義 56 第二節 模型之建構 57 第三節 結構之學習 58 第四節 模型之解釋 62 第五節 實證 65 第六節 輔助建模工具之設計 79 第五章 模型轉換法應用之二:整體性政策設計 82 第一節 政策設計程序 82 第二節 實證 83 第六章 結論及未來研究方向 103 附錄一 107 參考文獻 116 中文部分: 116 英文部分: 116 |
參考文獻 References |
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