Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

J. C. Rodríguez-Quiñonez, O. Sergiyenko, D. Hernandez-Balbuena, M. Rivas-Lopez, W. Flores-Fuentes, L. C. Basaca-Preciado

ABSTRACT

Many laser scanners depend on their mechanical construction to guarantee their measurements accuracy, however, the current computational technologies allow us to improve these measurements by mathematical methods implemented in neural networks. In this article we are going to introduce the current laser scanner technologies, give a description of our 3D laser scanner and adjust their measurement error by a previously trained feed forward back propagation (FFBP) neural network with a Widrow-Hoff weight/bias learning function. A comparative analysis with other learning functions such as the Kohonen algorithm and gradient descendent with momentum algorithm is presented. Finally, computational simulations are conducted to verify the performance and method uncertainty in the proposed system. More... »

PAGES

224-235

References to SciGraph publications

  • 2010-10. Optoelectronic system for mobile robot navigation in OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
  • 2012-09. History of infrared detectors in OPTO-ELECTRONICS REVIEW
  • 2013-03. Wide-angle vision for road views in OPTO-ELECTRONICS REVIEW
  • 2014-03. 3D image retrieval based on differential geometry and co-occurrence matrix in NEURAL COMPUTING AND APPLICATIONS
  • 2013-03. On-line range images registration with GPGPU in OPTO-ELECTRONICS REVIEW
  • 2013-03. 3D object recognition based on a geometrical topology model and extreme learning machine in NEURAL COMPUTING AND APPLICATIONS
  • 2013-03. 3D model classification based on nonparametric discriminant analysis with kernels in NEURAL COMPUTING AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.2478/s11772-014-0203-1

    DOI

    http://dx.doi.org/10.2478/s11772-014-0203-1

    DIMENSIONS

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