Open Access
Issue
MATEC Web Conf.
Volume 178, 2018
22nd International Conference on Innovative Manufacturing Engineering and Energy - IManE&E 2018
Article Number 07003
Number of page(s) 6
Section Innovation, Creativity, Learning and Education in Engineering
DOI https://doi.org/10.1051/matecconf/201817807003
Published online 24 July 2018
  1. R.E. Barlow, F. Proschan. Mathematical Theory of Reliability (J. Wiley & Sons, New York, 1995) [Google Scholar]
  2. S. Duer. Artificial neural network in the control process of object's states basis for organization of a servicing system of a technical objects. Neural Computing & Applications 21(1), 153-160 (2012) [CrossRef] [Google Scholar]
  3. S. Duer. Examination of the reliability of a technical object after its regeneration in a maintenance system with an artificial neural network. Neural Computing & Applications, 21(3), 523-534 (2012) [CrossRef] [Google Scholar]
  4. S. Duer, K. Zajkowski. Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network. Neural Computing & Applications 23(7), 2185-2197 (2013) [CrossRef] [Google Scholar]
  5. S. Duer, K. Zajkowski, R. Duer, J. Paś. Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network. Neural Computing & Applications 23(3-4), 913-925 (2013) [CrossRef] [Google Scholar]
  6. S. Duer. Applications of an artificial intelligence for servicing of a technical object. Neural Computing & Applications 22(5), 955-968 (2013) [CrossRef] [Google Scholar]
  7. M. Madan, M. Gupta, J. Liang, Homma N. Static and Dynamic Neural Networks, From Fundamentals to Advanced Theory (John Wiley & Sons, Inc, Hoboken, New Jersey, 2003) [Google Scholar]
  8. K. Zajkowski. The method of solution of equations with coefficients that contain measurement errors, using artificial neural network. Neural Computing and Applications 24(2), 431-439 (2014) [CrossRef] [Google Scholar]
  9. K. Zajkowski. An innovative hybrid insulation switch to enable/disable electrical loads without overvoltages. E3S Web of Conferences 19, 01033 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  10. K. Zajkowski. Settlement of reactive power compensation in the light of white certificates. E3S Web of Conferences, 19, 01037 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  11. S. Duer, K. Zajkowski, I. Płocha, R. Duer. Training of an artificial neural network in the diagnostic system of a technical object. Neural Computing & Applications 22(7), 1581-1590 (2013) [CrossRef] [Google Scholar]
  12. K. Zajkowski, S. Scaticailov. Determination of the environmental impact of reactive power compensation in the power grid. Nonconventional Technologies Review, Romania XX(2), 54-61 (2016) [Google Scholar]

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