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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0629101-131452 |
| Date | 29 June 2001 |
| Creators | Chen, Yao-Tsung |
| Contributors | Meng-Chang Chen, Chia-Ping Chen, Yi-Ming Tu, Bingchiang Jeng, Showing Young |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629101-131452 |
| Rights | withheld, Copyright information available at source archive |
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