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A Machine Learning Approach to Policy Optimization in System Dynamics Models
Author(s) -
Chen YaoTsung,
Tu YiMing,
Jeng Bingchiang
Publication year - 2011
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.1089
Subject(s) - system dynamics , computer science , process (computing) , mode (computer interface) , dynamics (music) , artificial neural network , artificial intelligence , mathematical optimization , machine learning , mathematics , physics , acoustics , operating system
The paper proposes a policy design method for system dynamics models based on recurrent neural networks. A policy maker first directly creates an arbitrary desired reference mode and run the algorithm to search for the most appropriate model(s) automatically to fit it. In the searching process, both the system structure and its parameter values evolve simultaneously. Several experiments are conducted to evaluate our approach. The results show that our approach is as good as or better than other comparable methods. Copyright © 2011 John Wiley & Sons, Ltd.