Application of artificial neural networks (ANN) method on the mechanical behavior simulation of syntactic foam plastics was discussed. Firstly, factors influencing on the mechanical behavior and mechanical properties simulated and predicted were separately taken as input and output quantities. Secondly, Four-layer neural networks model was established to simulate and predict the mechanical properties and constitutive relationship of syntactic foam plastics by means of back-propagation algorithm. The numerical results show that the trained ANN model can preferably simulate and predict the mechanical behavior of material, such as Young′s modulus, yield strength and stress-strain curves under different strain rates or temperatures. Additionally, by comparison among three different modified training methods, it is found that Bayesian regularization back-propagation has the best capacity of improving network generalization, Levenberg-Marquardt(LM) back-propagation would converge fastest, and gradient descent momentum & adaptive learning rate back-propagation need long-end iterative process before the same precision in calculation is achieved.
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