Abstract
The study of system dynamics starts from model construction and simulation to understand and solve dynamical complicated problems. Traditional approaches of modeling process depend on experts’ experiences and the trial-and-error pro- cedure, so it is difficult to guarantee a useful model. Because a system dynam- ics model is equivalent to a specially-designed artificial neural network, both of which operate under the same numerical propagation constraints, we use the ar- tificial neural network training algorithms and take advantage of historical data to assist system dynamics model construction. Experimental studies show that this approach is feasible.
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Ren, C., Chai, Y., Liu, Y. (2005). Adding Value to System Dynamics Modeling by Using Artificial Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_70
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DOI: https://doi.org/10.1007/11427445_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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