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This paper attempts to optimize the constitutive modelling of restrained recovery for the shape memory alloy (SMA). For this purpose, a backpropagation neural network (BPNN) model was developed to predict the restrained recovery of the SMA. The modelling data were collected from restrained recovery experiments on the SMA. Thanks to nonlinear function mapping and adaptation, the proposed model can learn the complete restrained recovery stress and temperature hysteresis of the SMA and predict thedoi:10.14704/nq.2018.16.5.1387 fatcat:xvv5wrtmsnb35ehmudkv24snba