A neural network approach for determination of Preisach model parameters under a sinusoidal induction at various frequencies

Moussaoui, D.; Bendjerad, A.; Oussalah, Mourad; Houassine, Hamza

doi:10.1016/j.physb.2005.10.027

Cited by 4 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen that the NN model can reliably predict the hysteretic behavior. Compared with the other predicting error curves of NNs with different hysteresis models (Li and Tan, 2004;Kuczmann and Iványi, 2002;Moussaoui et al, 2006; EC 29,3 Dlala and Arkkio, 2006;Zhang et al, 2009) and the convergent curve of the NN's training error (Li and Tan, 2004), the proposed model has some superiority.…”

Section: Experiments Validationmentioning

confidence: 94%

“…Kuczmann and Iványi (2002) developed a NN-based model for scalar hysteresis characteristics and the NNs are used to store the virgin curve and a set of the first-order reversal branches. Moussaoui et al (2006) presented a NN-based approach to predict the hysteretic loop. Dlala and Arkkio (2006) used neuro-fuzzy systems to model magnetic hysteresis by means of approximating the Everett function on the basis of the information provided only by the major loop.…”

Section: Introductionmentioning

confidence: 99%

“…In the recent decade, neural network (NN) has been widely and successfully used in many fields. Some attempts have been made to describe the map between output and input of PEA (Li and Tan, 2004;Kuczmann and Iványi, 2002;Moussaoui et al, 2006;Dlala and Arkkio, 2006;Zhang et al, 2009). Li and Tan (2004) employed a decomposition of the multi-valued mapping of the hysteresis into one-to-one mapping for the NNs approximation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators

Guo

Liu

Wang

2012

Engineering Computations

View full text Add to dashboard Cite

Purpose -Widely used in micro-position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator. Design/methodology/approach -The improved Preisach model is given: A wiping-out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping-out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators. Findings -Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators. Originality/value -The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.

show abstract

Section: Experiments Validationmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators

Guo

Liu

Wang

2012

Engineering Computations

View full text Add to dashboard Cite

show abstract

“…Ref. [18] used the MFNN to approximate the Preisach-hysteretic model that is also a kind of rate-independent hysteresis. Although the above-mentioned examples have shown that the MFNN can be used to model rate-independent hysteresis effectively, the modeling of ratedependent hysteresis is seldom found in literatures.…”

Section: Neural Network Based Identification For Rate-dependent Hystementioning

confidence: 99%

A neural networks based model for rate-dependent hysteresis for piezoceramic actuators

Dong

Tan

Chen

et al. 2008

Sensors and Actuators A: Physical

127

View full text Add to dashboard Cite

A new type of neural network preisach model for hysteresis in piezoelectric actuator

Liu

Wang

2010

2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)

View full text Add to dashboard Cite

A neural network approach for determination of Preisach model parameters under a sinusoidal induction at various frequencies

Cited by 4 publications

References 6 publications

Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators

Neural network hysteresis modeling with an improved Preisach model for piezoelectric actuators

A neural networks based model for rate-dependent hysteresis for piezoceramic actuators

A new type of neural network preisach model for hysteresis in piezoelectric actuator

Contact Info

Product

Resources

About