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 procedure, so it is difficult to guarantee a useful model. Because a system dynamics model is equivalent to a specially-designed artificial neural network, both of which operate under the same numerical propagation constraints, we use the artificial neural network training algorithms and take advantage of historical data to assist system dynamics model construction. Experimental studies show that this approach is feasible.
['Changrui Ren', 'Yueting Chai', 'Yi Liu']
Adding value to system dynamics modeling by using artificial neural network