Howells, W.G.J. and Sirlantzis, K. (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.
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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: | 20 Apr 2009 15:45 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/15487 (The current URI for this page, for reference purposes) |
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