Thesis-2009-Smith.pdf (20.36 MB)
Download fileAn evolutionary approach to optimising neural network predictors for passive sonar target tracking
thesis
posted on 2017-10-09, 09:07 authored by Duncan SmithObject 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.]
Funding
QinetiQ.
History
School
- Science
Department
- Computer Science
Publisher
© Duncan SmithPublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 2.5 Generic (CC BY-NC-ND 2.5) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/2.5/Publication date
2009Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.Language
- en