Processing of the information by the complex of neural networks in the distributed fiber-optical measuring systems
Y. N. Kulchin, E. V. Zakasovskaya

Institute of Automation and Control Processes, FEB RAS,
Far Eastern National University

Full text of article: Russian language.

Abstract:
The paper discusses tomography reconstruction of distributed physical fields by means of distributed fiber optical measuring systems (FOMN) for incomplete parallel schemes of measuring lines (ML) stacking. The approach is presented, consists in optimization of geometry of a measuring network for the purpose of the further application neural network methods of restoration of a full- image of investigated functions. Possibility of a choice and use of a suitable neural network from set of the several, in advance trained, neural networks of RBF- type is investigated.

Key words:
distributed fiber-optic measuring system, schemes of scanning, parallel beam tomography, radial basis function neural network (RBFNN).

References:

  1. Kulchin, Yu. N. Distributive Fiber Optical Measuring System / Yu. N. Kulchin - Moscow: Fizmatlit Publisher, 2001. – 272 p. – (in Russian).
  2. Kersey, A.D. A review of recent developments in fiber optic sensor technology / A.D. Kersey // Opt. Fiber Technol. – 1996. – Vol. 2, N 3. – P. 291-317.
  3. Mirovitskii D.I. Distributed and quasi-distributed fiber optic sensor // Meas. tech. – 1991. – N 11. – P. 43-44. – (in Russian).
  4. Natterer, F. Mathematics of Computerized Tomography/ F. Natterer– John Wiley & Sons Ltd., N. Y., 1986. –288 p.
  5. Filonin, O.V. Low angle Tomography/ O.V. Filonin. – Samara, SNC RAN Publisher, 2006. – 256 p. – (in Russian).
  6. Kulchin, Yu.N. Application of Radial Basis Function Neural Network for Information Processing in Fiber Optical Distributed Measuring Systems / Yu.N. Kulchin, E.V. Zakasovskaya // Optical Memory & Neural Networks (Information Optics). – 2008. – V. 17, N 4. – P. 317-327.
  7. Herman, G.T. Projections-Based Image Reconstruction. / G.T. Herman – In: «Basics of Reconstructive Tomography» – Moscow: “Mir” Publisher, 1983. – 352 p. – (in Russian).
  8. Zakasovskaya, E.V., Fadeev, V.V. Restoration of point influences by the fiber-optical network in view of a priori information // SPIE Proc. – APCOM. – 2007. – V. 6675.
  9. Kulchin, Yu.N. Artifacts suppression in limited data problem for parallel fiber optical measuring systems / Yu.N. Kulchin, E.V. Zakasovskaya // Optical Memory & Neural Networks (Information Optics). – 2009. – V. 18, N 3. – P. 171-180.
  10. Kulchin, Yu.N. Nonuniform schemes of measuring lines stacking in distributed fibre-optic systems/ Yu.N. Kul­chin, E.V. Zakasovskaya // Informatics and control systems – 2009. – N 3(21). – P. 61-71. – (in Russian).
  11. Haykin, S. Neural Networks: a Comprehensive Foundation / S. Haykin – New Jersey, Prentice Hall, 1999.
  12. Kulchin, Yu.N. Neural-like and algebraic modeling of projection data in parallel fiber optical tomography in limited-angle conditions / Yu.N. Kulchin, E.V. Zakasovskaya // Computer Optics. – 2009. – V. 33, N 3. – P. 318- 324. – (in Russian).

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