Automated Adaptation of Input and Output Data for a Weightless Artificial Neural Network

McElroy, Ben and Howells, Gareth (2011) Automated Adaptation of Input and Output Data for a Weightless Artificial Neural Network. International Journal of Database Theory and Application, 4 (3). pp. 49-58. ISSN 2005-4270. (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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The ability to adapt automated guided vehicles for employment to a range of practical situations can significantly enhance their usability in hazardous situations where security is a major concern and it is inadvisable for humans to enter. Robot guidance is still a very challenging issue computationally in both the academic and industrial worlds. Whilst considerable progress has been made in robotics in the last few decades, many still experience difficulties in the recognition of dynamically changing situations such as our daily environments. With so many different scenarios it is difficult to find one system that can effectively deal with both the expected and unexpected issues that may arise. This paper examines the possibility of manipulating the potential inputs and outputs to a system to tailor a better solution to the current problem. Weightless neural networks will be used as a classification tool to determine the direction of a robot in an open loop simulation.

Item Type: Article
Uncontrolled keywords: Robot guidance, weightless artificial neural networks, automated adaptation
Subjects: 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: 03 Nov 2011 17:27
Last Modified: 23 May 2014 09:09
Resource URI: (The current URI for this page, for reference purposes)
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