Jin, XiangjunShao, JieZhang, XinAn, WenweiMalekian, Reza2016-06-202016-05Jin, XJ, Shao, J, Zhang, X, An, W & Malekian, R 2016, 'Modeling of nonlinear system based on deep learning framework', Nonlinear Dynamics, vol. 83, no. 3, pp. 1327-1340.0924-090X (print)1573-269X (online)10.1007/s11071-015-2571-6http://hdl.handle.net/2263/53267A 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 pretraining 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 (CDPA) which operates in the ohmic and cut-off regions. According to error of time domain and spectrum, Back Propagation Neural Network model improved by RBMs (BP-RBMs) and ENN are compared of different input signals which are the simulated two-tone signal and actual square wave signal. The second system is a permanent magnet synchronous motor (PMSM) servo control system based on fuzzy PID control strategy. In terms of simulated and actual speed curves, BP-RBMs, DRM and ENN model are adopted on comparison respectively. It is shown by experimental results that the proposed model with fewer parameters and iteration number can reconstruct the nonlinear system accurately, and depict the memory effect, the nonlinear distortion and the dynamic performance of system precisely.en© Springer Science+Business Media Dordrecht 2015. The original publication is available at : http://link.springer.comjournal/11071.Nonlinear systemDeep learningRestricted Boltzmann Machines (RBMs)Deep Reconstruction Model (DRM)Elman neural network (ENN)Postprint Article