350 rub
Journal Neurocomputers №2 for 2017 г.
Article in number:
Adaptation in the system is synchronized in phase receiving and emitting objects in the phase distortion using artificial neural networks
Keywords: system receiving and emitting objects adaptation by last mean square adaptive array artificial neural network
Authors:
Nguyen Dang Tao - Post-graduate Student, Department of Radio Engineering and Cybernetics, Moscow Institute of physics and technology (State University)
Abstract:
The paper discusses the adaptation of the system synchronized with the phase of the receiving and emitting of objects by comparison with the reference signal in terms of phase distortion of the reference signal. To compensate for the phase distortion of the reference signal used in the performance of the system in the form of an artificial neural network, with subsequent training. Representation of systems and devices in the form of artificial neural networks used for solving various tasks. Such a representation enables the application of methods of optimization of parameters used in artificial neural networks for parameter optimization are presented in the form of systems or devices. The composition of the optimized parameters is determined by selecting the configuration of the artificial neural network. It is shown that the performance of the system is synchronized in phase receiving and emitting objects in the form of artificial neural network nonlinearity, the corresponding definition module of a complex number, the system can adapt by comparison with the reference signal in terms of phase distortion of the pattern due to the use as a reference only module reference signal.
Pages: 37-43
References

 

  1. Sazonov D.M. Mnogoehlementnye antennye sistemy. M.: Radiotekhnika. 2015.
  2. Adaptivnye antennye reshetki. Ucheb. posobie v 2-kh chastjakh / Pod red. V.A. Grigoreva. SPb.: Universitet ITMO. 2016.
  3. KHabarov A.V. Obedinenie antenn s neizvestnymi koordinatami v antennuju reshetku // Antenny. 2006. № 11. S. 7-11.
  4. Sejjdl P., Taufer I. Model korotkozamknutojj shhelevojj linii v vide iskusstvennojj nejjronnojj seti // Nejjrokompjutery: razrabotka, primenenie. 2009. № 5. S. 57-61.
  5. Tatuzov A.L. Nejjronnye seti v zadachakh radiolokacii. M: Radiotekhnika. 2009.
  6. Nguen Dang Tao. Issledovanie adaptacii sistemy sinkhronizirovannykh po faze priemoizluchajushhikh obektov, ispolzujushhejj iskusstvennye nejjronnye seti // Nejjrokompjutery: razrabotka, primenenie. 2017. № 1. S. 42-47.
  7. Galushkin A.I. Sintez mnogoslojjnykh sistem raspoznavanija obrazov. M.: EHnergija. 1974.
  8. Tarkhov D.A. Nejjrosetevye modeli i algoritmy. M.: Radiotekhnika. 2014.