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題名：使用SDM-PRN轉換法以輔助建構系統動力學模型及政策設計
作者：陳耀宗
作者(外文)：Yao-Tsung Chen
校院名稱：國立中山大學
系所名稱：資訊管理學系研究所
指導教授：鄭炳強
學位類別：博士
出版日期：2001
主題關鍵詞：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
原始連結：連回原系統網址new window
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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。
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.
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