Absolute Magnetic Encoder Design Based On RBF Neural Networks

Authors(2) :-N.Sangeetha, Rajashekar J.S

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.

Authors and Affiliations

EIE Department, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
Rajashekar J.S
EIE Department, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India

Rotary encoder, magnetic encoder, multisensor data fusion, hall effect sensor, ANN, RBF neural networks, analog angular measurement.

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Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 674-677
Manuscript Number : CSEIT1846126
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

N.Sangeetha, Rajashekar J.S, "Absolute Magnetic Encoder Design Based On RBF Neural Networks", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.674-677, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1846126

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