Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516313
Title: An evolutionary approach to optimising neural network predictors for passive sonar target tracking
Author: Smith, Duncan
ISNI:       0000 0001 2424 7743
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2009
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Abstract:
Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is not specific to a single classifier.
Supervisor: Not available Sponsor: QinetiQ
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.516313  DOI: Not available
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