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 | 
| Institutional Unit: | Schools > School of Engineering, Mathematics and Physics > Engineering | 
| Former Institutional Unit: | Image and Information Engineering 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: | 20 May 2025 10:32 UTC | 
| Resource URI: | https://kar.kent.ac.uk/id/eprint/15487 (The current URI for this page, for reference purposes) | 
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