Hopfield Neural Network Identification for Prandtl-Ishlinskii Hysteresis Nonlinear System
A new Hopfield Neural Network (HNN) identification approach is proposed for a Prandtl-Ishlinskii (P-I) hysteresis nonlinear system. Firstly, The P-I hysteresis nonlinear system is transformed into canonical form by linear state transformation with \(B^\perp \) to suit the identification design. Then, we define a energy function E which is constituted by the transformed canonical state space system coefficients. Another suitable energy function \(E_n\) is proposed with HNN to identify the hysteresis system. Finally, simulation results have verified the performance of the proposed identification.
KeywordsHNN Identification Hysteresis P-I model
This work is Supported by the National Natural Science Foundation of China (61433003), Shandong Natural Science Foundation of China(ZR2017MF048), Shandong Key Research and Development Programme (2016GGX105013), Shandong Science and technology program of higher education (J17KA214), Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2016RCJJ035), Tai’an Science and Technology development program (2017GX0017).
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