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Neural network based identification of Preisach-type hysteresis in piezoelectric actuator using hysteretic operator
- Source :
- Sensors and Actuators A: Physical. 126:306-311
- Publication Year :
- 2006
- Publisher :
- Elsevier BV, 2006.
-
Abstract
- A neural network based approach of identification for Preisach-type hysteresis is proposed. In this method, a hysteretic operator is introduced to transform the multi-valued mapping of hysteresis into a one-to-one mapping so that neural networks can be applied to the approximation of the behavior of hysteresis. The proposed model has a simple architecture that simplifies identification procedure and is available to different operating conditions. Finally, an example of simulation and the result of modeling the hysteresis existing in a piezoelectric actuator based on the proposed method are presented.
- Subjects :
- Preisach model of hysteresis
Engineering
Artificial neural network
business.industry
Metals and Alloys
Type (model theory)
Condensed Matter Physics
Surfaces, Coatings and Films
Electronic, Optical and Magnetic Materials
Condensed Matter::Materials Science
Identification (information)
Hysteresis
Operator (computer programming)
Simple (abstract algebra)
Control theory
Condensed Matter::Superconductivity
Piezoelectric actuators
Electrical and Electronic Engineering
business
Instrumentation
Subjects
Details
- ISSN :
- 09244247
- Volume :
- 126
- Database :
- OpenAIRE
- Journal :
- Sensors and Actuators A: Physical
- Accession number :
- edsair.doi...........5d400b1bf9eeefe4d8b00d89e01ae8e8
- Full Text :
- https://doi.org/10.1016/j.sna.2005.10.023