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Training oscillatory neural networks using natural gradient particle swarm optimization

Published online by Cambridge University Press:  15 April 2014

Hamed Shahbazi ,
Kamal Jamshidi ,
Amir Hasan Monadjemi  and
Hafez Eslami Manoochehri
Hamed Shahbazi*
Affiliation:
Faculty of Engineering, University of Isfahan, Isfahan, Iran
Kamal Jamshidi
Affiliation:
Faculty of Engineering, University of Isfahan, Isfahan, Iran
Amir Hasan Monadjemi
Affiliation:
Faculty of Engineering, University of Isfahan, Isfahan, Iran
Hafez Eslami Manoochehri
Affiliation:
Faculty of Engineering, University of Isfahan, Isfahan, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

In this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

Keywords

Central pattern generatorsHumanoid robotsNeural networksPolicy gradient learningWalkingSensory feedbacks
Type
Articles
Information
Robotica , Volume 33 , Issue 7 , August 2015 , pp. 1551 - 1567
Copyright
Copyright © Cambridge University Press 2014 

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