![](/images/graphics-bg.png)
Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach
Joint Authors
Firouzi, Mohsen
Zakerzadeh, Mohammad Reza
Sayyaadi, Hassan
Shouraki, Saeed Bagheri
Source
Journal of Applied Mathematics
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-22, 22 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-04-20
Country of Publication
Egypt
No. of Pages
22
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zakerzadeh, Mohammad Reza& Firouzi, Mohsen& Sayyaadi, Hassan& Shouraki, Saeed Bagheri. 2011. Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach. Journal of Applied Mathematics،Vol. 2011, no. 2011, pp.1-22.
https://search.emarefa.net/detail/BIM-473159
Modern Language Association (MLA)
Zakerzadeh, Mohammad Reza…[et al.]. Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach. Journal of Applied Mathematics No. 2011 (2011), pp.1-22.
https://search.emarefa.net/detail/BIM-473159
American Medical Association (AMA)
Zakerzadeh, Mohammad Reza& Firouzi, Mohsen& Sayyaadi, Hassan& Shouraki, Saeed Bagheri. Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach. Journal of Applied Mathematics. 2011. Vol. 2011, no. 2011, pp.1-22.
https://search.emarefa.net/detail/BIM-473159
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-473159