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An evolutionary approach to optimising neural network predictors for passive sonar target tracking

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posted on 09.10.2017 by Duncan Smith
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.]

Funding

QinetiQ.

History

School

  • Science

Department

  • Computer Science

Publisher

© Duncan Smith

Publisher 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

2009

Notes

A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.

Language

en

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