Abstract
An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.
| Original language | English |
|---|---|
| Pages (from-to) | 1138-1151 |
| Number of pages | 14 |
| Journal | Neurocomputing |
| Volume | 72 |
| Issue number | 4-6 |
| DOIs | |
| Publication status | Published - 2009 Jan 1 |
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Keywords
- Gradient descent method
- Linear ultrasonic motors
- Type-2 fuzzy logic system
- Type-2 fuzzy neural network
- X-Y-Theta motion control
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors. / Lin, Faa Jeng; Chen, Syuan Yi; Chou, Po Huan; Shieh, Po Huang.
In: Neurocomputing, Vol. 72, No. 4-6, 01.01.2009, p. 1138-1151.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Interval type-2 fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors
AU - Lin, Faa Jeng
AU - Chen, Syuan Yi
AU - Chou, Po Huan
AU - Shieh, Po Huang
PY - 2009/1/1
Y1 - 2009/1/1
N2 - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.
AB - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed to control the position of an X-Y-Theta (X-Y-θ) motion control stage using linear ultrasonic motors (LUSMs) to track various contours. The IT2FNN, which combines the merits of interval type-2 fuzzy logic system (FLS) and neural network, is developed to simplify the computation and to confront the uncertainties of the X-Y-θ motion control stage. Moreover, the parameter learning of the IT2FNN based on the supervised gradient descent method is performed on line. The experimental results show that the tracking performance of the IT2FNN is significantly improved compared to type-1 FNN.
KW - Gradient descent method
KW - Linear ultrasonic motors
KW - Type-2 fuzzy logic system
KW - Type-2 fuzzy neural network
KW - X-Y-Theta motion control
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U2 - 10.1016/j.neucom.2008.02.013
DO - 10.1016/j.neucom.2008.02.013
M3 - Article
AN - SCOPUS:58149456900
VL - 72
SP - 1138
EP - 1151
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
IS - 4-6
ER -