Improving Robotic System Robustness via a Generalised Formal Artificial Neural System

Howells, Gareth and Sirlantzis, Konstantinos (2008) Improving Robotic System Robustness via a Generalised Formal Artificial Neural System. In: 2008 ECSIS Symposium on Learning and Adaptive Behaviour in Robotic Systems (LAB-RS 2008), 06-08 August 2008, Edinburgh, UK. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Abstract

A major concern for robotic guidance systems is that a temporary or permanent failure of a given sensor within the system will erroneously trigger a potential system failure state. This paper introduces a generalised artificial neural system which is capable of addressing such problems by means of the inclusion of a weight value able to incorporate a distinct failure value. This will serve to significantly improve the performance and reliability of the guidance system

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 20 Apr 2009 15:45
Last Modified: 23 May 2014 09:16
Resource URI: https://kar.kent.ac.uk/id/eprint/15487 (The current URI for this page, for reference purposes)
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