An evolutionary approach to optimising neural network predictors for passive sonar target tracking
Duncan Smith
2134/26870
https://repository.lboro.ac.uk/articles/thesis/An_evolutionary_approach_to_optimising_neural_network_predictors_for_passive_sonar_target_tracking/9406673
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.]
2017-10-09 09:07:21
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Information and Computing Sciences not elsewhere classified