The artificial neural network modeling of dynamic hysteresis phase-diagram: Application on mean-field ising hysteresis

• Main Authors: Wimalin Laosiritaworn, Kanokwan Kanchiang, Yongyut Laosiritaworn
• Format: Book Series
• Published: 2018
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84886782372&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/47509
• Institution: Chiang Mai University

This work used Artificial Neural Network (ANN) to investigate the hysteresis behavior of the Ising spins in structures ranging from one- to two- and three-dimensions. The equation of magnetization motion under the mean-field picture was solved using the Runge-Kutta method to extract the Ising hysteresis loops with varying the temperature, the external magnetic field parameters and the system structure (via the variation of number of nearest neighboring spins). The ANN was then used to establish relationship among parameters via Back Propagation technique in ANN training. With the trained networks, the ANN was used to predict hysteresis data, with an emphasis on the dynamic critical point, and compared with the actual target data. The predicted and the target data were found to agree well which indicates that the ANN functions well in modeling hysteresis behavior and its critical phase-diagram across systems with different structures. © (2013) Trans Tech Publications, Switzerland.