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Improving Robotic System Robustness via a Generalised Formal Artificial Neural System

Howells, Gareth, 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), Edinburgh, UK. . pp. 23-28. IEEE Computer Society (doi:10.1109/lab-rs.2008.12) (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) (KAR id:15487)

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.
Official URL:
https://doi.org/10.1109/lab-rs.2008.12

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)
DOI/Identification number: 10.1109/lab-rs.2008.12
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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Konstantinos Sirlantzis
Date Deposited: 20 Apr 2009 15:45 UTC
Last Modified: 05 Nov 2024 09:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/15487 (The current URI for this page, for reference purposes)

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