International Journal of New Developments in Engineering and Society, 2023, 7(9); doi: 10.25236/IJNDES.2023.070908.
Xiangyu Sun
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
With the continuous improvement of sensor technology, machine learning and data processing capabilities, underwater navigation gradually turns to the data-driven model. In this paper, we make full use of the data in the appendix to make a reasonable judgment on the classification and prediction of underwater adaptive areas, and evaluate the performance. K-means clustering method is used to cluster the characteristics of gravity anomalies in different regions. When the number of clustering centers is set from 2 to 11:00, the clustering results show that when the number of clusters is 2, 3, 5, 7, 8, 9, all 100 iterations converge, and the clustering effect is good. When the number of clustering is 2, 3 and 5, the sum of the best distance is larger, and the distribution of contour coefficient has negative expansion; when the number of clustering is 7 and 8, the distribution of contour coefficient has negative expansion; when the number of clustering is 9, the contour coefficient is larger, the distribution is uniform on the front, the part of the contour coefficient less than 0 is very small, the convergence in the iterative process, the optimal distance and the sum of the optimal distance is relatively small: 63711.2. Therefore, when the number of clusters is 9, the fitting effect of each region is the best. Mark subclass 1 as area 1, mark subclass 2 as area 2, and so on.
K-means clustering, Data-driven, Underwater Navigation, Adaptive region Classification
Xiangyu Sun. Adaptive Region Classification and Prediction of Underwater Navigation Based on Data-driven. International Journal of New Developments in Engineering and Society (2023) Vol.7, Issue 9: 42-47. https://doi.org/10.25236/IJNDES.2023.070908.
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