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Hopfield Neural Network Identification for Prandtl-Ishlinskii Hysteresis Nonlinear System

  • Xuehui Gao
  • Shubo Wang
  • Ruiguo Liu
  • Bo Sun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

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.

Keywords

HNN Identification Hysteresis P-I model 

Notes

Acknowledgements

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Electrical EngineeringShandong University of Science and TechnologyTai’anChina
  2. 2.College of Automation and Electrical EngineeringQingdao UniversityQingdaoPeople’s Republic of China

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