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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