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Details of Grant 

EPSRC Reference: GR/K55325/01
Title: STRATEGIES FOR APPLYING NEURAL COMPUTING TO TIME- VARYING SIGNALS & APPLICATION TO TYRE QUALITY CONTROL
Principal Investigator: Harris, Dr T
Other Investigators:
Allinson, Professor NM
Researcher Co-Investigators:
Project Partners:
Pre Nexus Migration
Department: Design
Organisation: Brunel University London
Scheme: Standard Research (Pre-FEC)
Starts: 30 October 1995 Ends: 29 October 1997 Value (£): 84,472
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:  
Summary on Grant Application Form
This proposal is made in response to the Neural Computing - Key Questions CallThe project will address some of the difficulties involved in the practical application of neural computing to time-varying signals. The main questions include when and how to re-train and in the representation of prior knowledge. The test-bed application and industrial support is provided from the tyre industry by Dunlop SP Tyres UK Ltd. The results will be generic enough, however, to benefit a wide range of industrial sectors.The work will mainly focus on the application of Kohonen Self-Organising Map Networks and Logical Weightless Networks and will primarily address question 2 of the call: What is the optimum strategy when applying neural computing to time-varying data? . The particular questions of when and how the network should be re-trained and what and how prior knowledge can be incorporated will be of critical importance to the work. The optimum architectures for on-line re-training, reliability and robustness will also be addressed. Additional questions address the data representation area, in particular the basic time-varying or transformed data. All these questions will be addressed against the desire for robust and cost effective, practical solutions.
Key Findings
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Summary
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Organisation Website: http://www.brunel.ac.uk