The study of system dynamics starts from model construction and simulation to understand and solve dynamical complicated problems. Traditionally approaches of modeling process depend on an expert¡¦s experiences and the trial & error procedure.
Chen¡¦s research proposes a transformation method that could map a System Dynamics Model (SDM) to a specially designed Partial Recurrent Network (PRN). Thus he could use the neural network training algorithm to assist model construction and policy design.
In this paper, we will introduce a Genetic Algorithm (GA) in the model building process, which encodes a PRN into a string and uses an evolution process to select a best solution. The algorithm not only improves the PRN training, but also generates more candidate models for consideration. Thus, it enhances the SDM-PRN transformation¡¦s usability.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0815103-065607 |
| Date | 15 August 2003 |
| Creators | Luo, Zheng-Hong |
| Contributors | San-Yi Huang, Yuh-Jiuan Tsay, Bing-Chiang Jeng |
| 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-0815103-065607 |
| Rights | campus_withheld, Copyright information available at source archive |
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