Title page for etd-0629101-131452


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URN etd-0629101-131452
Author Yao-Tsung Chen
Author's Email Address ytchen@vensu.mis.nsysu.edu.tw
Statistics This thesis had been viewed 5627 times. Download 2796 times.
Department Information Management
Year 2000
Semester 2
Degree Ph.D.
Type of Document
Language zh-TW.Big5 Chinese
Title The Use of SDM-PRN Transformation for System Dynamics Model Construction and Policies Design
Date of Defense 2000-06-21
Page Count 128
Keyword
  • System Dynamics
  • Machine Learning
  • Policy Design
  • Model Construction
  • Neural Network
  • 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.
    Advisory Committee
  • Meng-Chang Chen - chair
  • Yi-Ming Tu - co-chair
  • Showing Young - co-chair
  • Chia-Ping Chen - co-chair
  • Bingchiang Jeng - advisor
  • Files
  • etd-0629101-131452.pdf
  • indicate accessible in a year
    Date of Submission 2001-06-29

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