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Absolute Magnetic Encoder Design Based On RBF Neural Networks

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  • N.Sangeetha
  • Rajashekar J.S

Abstract

This paper proposes absolute magnetic encoder design for analog angular measurement using multi-sensor data-fusion based on Radial Basis Function (RBF) neural networks. Multiple linear Hall effect sensors and a magnet are used to realize the analog angular output. RBF neural networks are used to approximate multi-dimensional nonlinear function between the sensor values and angular position of the magnet. The parameters of the RBF network are determined by supplying the data for multiple sensor values and the corresponding angular position of the magnet. Trained RBF neural network can be used to obtain the analog angle output for the given sensor inputs and it can be implemented using 8 or 16-bit microcontroller. This design of the encoder allows flexibility in terms of placement of the sensors.

Suggested Citation

  • N.Sangeetha & Rajashekar J.S, 2018. "Absolute Magnetic Encoder Design Based On RBF Neural Networks," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 4(6), pages 674-677, May.
    • Handle: RePEc:jbh:ijsrcs:v4:y2018:i6:id:hcseit1846126
    Note: Article URL: https://ijsrcseit.com/CSEIT1846126
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