Journal of Applied Mathematics
Volume 2011 (2011), Article ID 458768, 22 pages
Research Article

Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach

1School of Mechanical Engineering, Sharif University of Technology, P.O. Box 11155-9567, Tehran, Iran
2Electrical Engineering Department, Artificial Creature Lab, Sharif University of Technology, P.O. Box 11155-9567, Tehran, Iran

Received 29 September 2010; Revised 26 December 2010; Accepted 15 February 2011

Academic Editor: I. Stamova

Copyright © 2011 Mohammad Reza Zakerzadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost.