Dynamic hysteresis modelling of magnetic materials by using a neural network approach
- Resource Type
- Conference
- Authors
- Laudani, Antonino; Lozito, Gabriele Maria; Fulginei, Francesco Riganti
- Source
- 2014 AEIT Annual Conference - From Research to Industry: The Need for a More Effective Technology Transfer (AEIT) AEIT Annual Conference - From Research to Industry: The Need for a More Effective Technology Transfer (AEIT), 2014. :1-6 Sep, 2014
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Artificial neural networks
Magnetic hysteresis
Training
Magnetic devices
Time-frequency analysis
Arrays
Magnetic Hysteresis
Neural Networks
Magnetodynamic
Magnetic losses
- Language
The modelling of the dynamic behavior of hysteretic materials and devices must take into account magnetodynamic effects. In the present paper these tasks are simultaneously modelled by means of an ad-hoc Neural System (NS) based on an array of 3-input 1-output Feed Forward NNs. Each NN is aimed to a particular typology of the excitation field (prediction of flux density from a known waveform of the magnetic field strength or vice-versa) and manages just a fixed portion of the dynamic hysteresis loop. The whole hysteretic curve is simulated by linking the evaluations made by different NNs of the NS. The NS is able to perform the simulation of any kind of dynamic loop (saturated and non-saturated, symmetric or asymmetric) generated by any assigned arbitrarily distorted excitations into a fixed range of frequencies. Numerical validations are presented both on a "virtual magnetic device" and on a non-oriented Fe-(3 wt%) Si laminations (thickness ∼0.35 mm).