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

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Paladyn

Abstract

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.

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Authors and Affiliations

  1. Cybernetics, School of Systems Engineering, University of Reading, Reading, UK

    C. D. Bamford & R. J. Mitchell

Authors
  1. C. D. Bamford
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  2. R. J. Mitchell
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Correspondence to R. J. Mitchell.

Additional information

This paper has been partially published in the IEEE 9th International Conference on Cybernetic Intelligent Systems, 2010, Reading, UK

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Bamford, C.D., Mitchell, R.J. Modular non-computational-connectionist-hybrid neural network approach to robotic systems. Paladyn 2, 126–133 (2011). https://doi.org/10.2478/s13230-012-0003-6

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  • DOI: https://doi.org/10.2478/s13230-012-0003-6

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