Autonomous Data Collection Using a Self-Organizing Map
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00315458" target="_blank" >RIV/68407700:21230/18:00315458 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7888568/" target="_blank" >http://ieeexplore.ieee.org/document/7888568/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2017.2678482" target="_blank" >10.1109/TNNLS.2017.2678482</a>
Alternative languages
Result language
angličtina
Original language name
Autonomous Data Collection Using a Self-Organizing Map
Original language description
The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
2162-2388
Volume of the periodical
29
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
1703-1715
UT code for WoS article
000430729100025
EID of the result in the Scopus database
2-s2.0-85017163884