A new machine learning paradigm for terrain reconstruction
Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the...
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| Fformat: | Journal Article |
| Iaith: | English |
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2010
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| Mynediad Ar-lein: | https://hdl.handle.net/10356/91543 http://hdl.handle.net/10220/6308 |
| _version_ | 1826111998389125120 |
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| author | Huang, Guang-Bin Yeu, Thomas Chee Wee Lim, Meng-Hiot Agarwal, Amit Ong, Yew Soon |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Huang, Guang-Bin Yeu, Thomas Chee Wee Lim, Meng-Hiot Agarwal, Amit Ong, Yew Soon |
| author_sort | Huang, Guang-Bin |
| collection | NTU |
| description | Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles
in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM. |
| first_indexed | 2024-10-01T03:00:03Z |
| format | Journal Article |
| id | ntu-10356/91543 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:00:03Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/915432020-03-07T14:02:40Z A new machine learning paradigm for terrain reconstruction Huang, Guang-Bin Yeu, Thomas Chee Wee Lim, Meng-Hiot Agarwal, Amit Ong, Yew Soon School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM. Published version 2010-08-17T05:48:29Z 2019-12-06T18:07:34Z 2010-08-17T05:48:29Z 2019-12-06T18:07:34Z 2006 2006 Journal Article Yeu, C. W., Lim, M. H., Huang, G. B., Agarwal, A., & Ong, Y. S. (2006). A new machine learning paradigm for terrain reconstruction. Geoscience and Remote Sensing Letters. 3(3), 382-386. 1545-598X https://hdl.handle.net/10356/91543 http://hdl.handle.net/10220/6308 10.1109/LGRS.2006.873687 en Geoscience and remote sensing letters © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. 5 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Huang, Guang-Bin Yeu, Thomas Chee Wee Lim, Meng-Hiot Agarwal, Amit Ong, Yew Soon A new machine learning paradigm for terrain reconstruction |
| title | A new machine learning paradigm for terrain reconstruction |
| title_full | A new machine learning paradigm for terrain reconstruction |
| title_fullStr | A new machine learning paradigm for terrain reconstruction |
| title_full_unstemmed | A new machine learning paradigm for terrain reconstruction |
| title_short | A new machine learning paradigm for terrain reconstruction |
| title_sort | new machine learning paradigm for terrain reconstruction |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | https://hdl.handle.net/10356/91543 http://hdl.handle.net/10220/6308 |
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