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A novel modeling based on deep learning framework which can exactly manifest the characteristics of nonlinear system is proposed in this paper. Specifically, a Deep Reconstruction Model (DRM) is defined integrating with the advantages of the deep learning and Elman neural network (ENN). The parameters of the model are initialized by performing unsupervised pre-training in a layer-wise fashion using restricted Boltzmann machines (RBMs) to provide a faster convergence rate for modeling. ENN can be used to manifest the memory effect of system. To validate the proposed approach, two different nonlinear systems are used for experiments. The first one corresponds to the class-D power amplifier which operates in the ohmic and cutoff regions. According to error of time domain and spectrum, back propagation neural network model improved by RBMs (BP-RBMs) and ENN are compared with different …