Magnetic hysteresis modeling via feed-forward neural networks

1998 ◽  
Vol 34 (3) ◽  
pp. 623-628 ◽  
Author(s):  
C. Serpico ◽  
C. Visone
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feed Forward ◽  
Hysteresis Modeling ◽  
Feed Forward Neural Networks

Isotropic vector hysteresis modeling with feed-forward neural networks

2002 ◽  
Vol 91 (10) ◽  
pp. 8322 ◽  
Author(s):  
Dimitre Makaveev ◽  
Luc Dupré ◽  
Marc De Wulf ◽  
Jan Melkebeek
Keyword(s):  
Neural Networks ◽  
Feed Forward ◽  
Isotropic Vector ◽  
Hysteresis Modeling ◽  
Feed Forward Neural Networks

A Novel Composite Neural Network for Hysteresis Modeling in Piezoelectric Actuators

2009 ◽  
Author(s):  
Mohamad Fazli ◽  
Seyed Mahdi Rezaei ◽  
Mohamad Zareienejad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Piezoelectric Actuators ◽  
Maximum Error ◽  
Feed Forward ◽  
Positioning Systems ◽  
Time Dynamic ◽  
Operational Conditions ◽  
Hysteresis Modeling ◽  
Feed Forward Neural Networks

Piezoelectric actuators are convenient for micro positioning systems. Inherent hysteresis is one of the drawbacks in use of these actuators. Precise control of this actuator under changing of environmental and operational conditions, without modeling of hysteresis, is impossible. Neural networks can be used for this modeling. The ordinary feed forward neural networks can not train time dynamic relationship between input and output. Thus a certain type of networks called time delay feed forward neural networks (TDNN), are developed and is used in this paper. In the previous researches in this field, the important effect of loaded force on the actuator is ignored. This can increase the positioning error remarkably. Especially when these actuators are used in the precise grinding or machining operations. In this paper, neural network is used for hysteresis modeling with attention to the important effect of loaded force. After modeling, inverse hysteresis model is used as compensator in a feed forward way to linearize the input-output relationship. Then using PI closed loop controller and selecting suitable coefficient for it, the maximum error was decreased to less than 2 percent of the working amplitude.

Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

2001 ◽  
Vol 89 (11) ◽  
pp. 6737-6739 ◽  
Author(s):  
Dimitre Makaveev ◽  
Luc Dupré ◽  
Marc De Wulf ◽  
Jan Melkebeek
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feed Forward ◽  
Feed Forward Neural Networks

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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