Modeling of nonlinear system based on deep learning framework

Show simple item record Jin, Xiangjun Shao, Jie Zhang, Xin An, Wenwei Malekian, Reza 2016-06-20T08:58:53Z 2016-05
dc.description.abstract 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 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_ZA
dc.description.department Electrical, Electronic and Computer Engineering en_ZA
dc.description.embargo 2017-05-31
dc.description.librarian hb2016 en_ZA
dc.description.sponsorship This work was supported in part by the Foundation of Key Laboratory of China’s Education Ministry and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. en_ZA
dc.description.uri en_ZA
dc.identifier.citation Jin, 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. en_ZA
dc.identifier.issn 0924-090X (print)
dc.identifier.issn 1573-269X (online)
dc.identifier.other 10.1007/s11071-015-2571-6
dc.language.iso en en_ZA
dc.publisher Springer en_ZA
dc.rights © Springer Science+Business Media Dordrecht 2015. The original publication is available at : http://link.springer.comjournal/11071. en_ZA
dc.subject Nonlinear system en_ZA
dc.subject Deep learning en_ZA
dc.subject Restricted Boltzmann Machines (RBMs) en_ZA
dc.subject Deep Reconstruction Model (DRM) en_ZA
dc.subject Elman neural network (ENN) en_ZA
dc.title Modeling of nonlinear system based on deep learning framework en_ZA
dc.type Postprint Article en_ZA

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