Neural network based model for incorporating the thermal effect on the magnetic hysteresis of the 3F3 material

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications.

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Neural network modeling of magnetic hysteresis

2008 IEEE International Conference on Emerging Technologies and Factory Automation ◽

10.1109/etfa.2008.4638563 ◽

2008 ◽

Author(s):

V. Akbarzadeh ◽

M. Davoudpour ◽

A. Sadeghian

Keyword(s):

Neural Network ◽

Network Modeling ◽

Magnetic Hysteresis ◽

Neural Network Modeling

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Adaptive regulating of automotive mono-tube hydraulic adjustable dampers using gray neural network–based compensation system

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407018800580 ◽

2018 ◽

Vol 233 (10) ◽

pp. 2532-2545 ◽

Cited By ~ 6

Author(s):

Xinbo Ma ◽

Pak Kin Wong ◽

Jing Zhao

Keyword(s):

Neural Network ◽

Thermal Effect ◽

Analytical Model ◽

Heat Dissipation ◽

Compensation System ◽

Public Attention ◽

Damping Force ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Simulation Results

With the development of the controllable suspension systems, the mono-tube hydraulic adjustable damper has attracted great public attention with the advantages such as good heat dissipation, less power, fast response, durable, reliable, and simple structure. However, the unknown regulating mechanism modeling impedes the practical application of the mono-tube hydraulic adjustable damper. To model the regulating mechanism, this paper analytically studies the behavior of the mono-tube hydraulic adjustable damper via developing an analytical model and thermal effect equations for the use of engineering design. Then, the mono-tube hydraulic adjustable damper is tested in an integral shock absorber testing system to verify the accuracy of model and equations. On the basis of the verified analytical model and thermal effect equations, a compensation system with gray neural network algorithm is originally designed to model the regulating mechanism of the mono-tube hydraulic adjustable damper, thus achieving the desired damping force adaptively and accurately at various working conditions by obtaining the required rotary angle of the adjustment rod. The simulation results and experimental results show that the characteristic analyses of mono-tube hydraulic adjustable damper are reliable. Meanwhile, the simulation results of the gray neural network algorithm also indicate that the proposed compensation system can provide an exact regulating mechanism model for the mono-tube hydraulic adjustable damper and the proposed gray neural network algorithm is superior to the traditional neural network algorithm.

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