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Three methods of training multi-layer perceptrons to model a robot sensor

Published online by Cambridge University Press:  09 March 2009

D. T. Pham  and
S. Sagiroglu
D. T. Pham
Affiliation:
Intelligent Systems Laboratory, Systems Engineering Division, School of Engineering, University of Wales College of Cardiff, P.O. Box 917, Cardiff, CF2 1XH (UK)
S. Sagiroglu
Affiliation:
Intelligent Systems Laboratory, Systems Engineering Division, School of Engineering, University of Wales College of Cardiff, P.O. Box 917, Cardiff, CF2 1XH (UK)
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Summary

This paper discusses three methods of training multi-layer perceptrons (MLPs) to model a six-degrees-of- freedom inertial sensor. Such a sensor is designed for use with a robot to determine the location of objects it has to pick up. The sensor operates by measuring parameters related to the inertia of an object and computing its location from those parameters. MLP models are employed for part of the computation. They are trained to output the orientation of the object in response to an input pattern that includes the period of natural vibration of the sensor on which the object rests. After reviewing the working principle of the sensor, the paper describes the three MLP training methods (backpropagation, optimisation using the Levenberg-Marquardt algorithm, evolution based on the genetic algorithm) and presents the experimental results obtained.

Keywords

Neural networksMulti-layer perceptronsBackpropagation; Genetic algorithmLevenberg-Marquardt algorithmInertial sensorRobot sensor.

Information

Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 531 - 538
Copyright
Copyright © Cambridge University Press 1995

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References

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