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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/26870

Title: An evolutionary approach to optimising neural network predictors for passive sonar target tracking
Authors: Smith, Duncan
Issue Date: 2009
Publisher: © Duncan Smith
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. [Continues.]
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.
Sponsor: QinetiQ.
URI: https://dspace.lboro.ac.uk/2134/26870
Appears in Collections:PhD Theses (Computer Science)

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