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

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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.
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Bibliogr. 42 poz.. il., tab., wykr.
  • Faculty of Engineering, Autonomous University of Baja California, Mexicali, Baja California, Mexico
  • Engineering Institute, Autonomous University of Baja California, Mexicali, Baja California, Mexico
  • Faculty of Engineering, Autonomous University of Baja California, Mexicali, Baja California, Mexico
  • Engineering Institute, Autonomous University of Baja California, Mexicali, Baja California, Mexico
  • Faculty of Engineering, Autonomous University of Baja California, Mexicali, Baja California, Mexico
  • Engineering School, CETYS, Mexicali, Baja California, Mexico
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