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The Use of Genetic Algorithms for System Dynamics Model Construction

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.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0815103-065607
Date15 August 2003
CreatorsLuo, Zheng-Hong
ContributorsSan-Yi Huang, Yuh-Jiuan Tsay, Bing-Chiang Jeng
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0815103-065607
Rightscampus_withheld, Copyright information available at source archive

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