Accessibility navigation


Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Bamford, C. D. and Mitchell, R. J. (2011) Modular non-computational-connectionist-hybrid neural network approach to robotic systems. Paladyn. Journal of Behavioral Robotics, 2 (3). pp. 126-133. ISSN 2081-4836 (special issue: Cybernetic Approaches to Robotics)

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.2478/s13230-012-0003-6

Abstract/Summary

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:28244
Uncontrolled Keywords:neural networks – robotics – spiking neurons – hybrid systems
Publisher:Versita; Springer

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation