Abstract
The article introduces a hybrid intelligent system for point localization in 3D Euclidean space. There are two models presented. The first one is based on neural networks and the second one represents a classical approach. The classical model calculates Euclidean distances between two points in the defined domain. As regards the experimental study, we proposed appropriate topologies of the systems that depend on the required accuracy. At first, we identified distances between a randomly generated point and a reference points in the defined domain. Then a neural network uses the obtained distances as its inputs to determine the actual position of the point in the domain space. The experimental study was repeated several times. All obtained results are mutually compared in the conclusion.
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Acknowledgments
The research described here has been financially supported by University of Ostrava grant SGS/PřF/2014. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the sponsors.
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Jarusek, R., Volna, E., Kolcun, A., Kotyrba, M. (2014). Hybrid Intelligent System for Point Localization. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-319-06740-7_9
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DOI: https://doi.org/10.1007/978-3-319-06740-7_9
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