Application of neural networks to the inverse light scattering problem for spheres

Zbigniew Ulanowski, Z. Wang, Paul H. Kaye, Ian Ludlow

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)
161 Downloads (Pure)

Abstract

A new approach suitable for solving inverse problems in multi-angle light scattering is presented. The method takes advantage of multidimensional function approximation capability of radial basis function (RBF) neural networks. An algorithm for training the networks is described in detail. It is shown that the radius and refractive index of homogenous spheres can be recovered accurately and quickly, with maximum relative errors of the order of 10-3 and mean errors as low as 10-5. The influence of the angular range of available scattering data on the loss of information and inversion accuracy is investigated and it is shown that more than two thirds of input data can be removed before substantial degradation of accuracy occurs.
Original languageEnglish
Pages (from-to)4027-4033
JournalApplied Optics
Volume37
Issue number18
Publication statusPublished - 1998

Keywords

  • light scattering
  • particle sizing
  • sphere
  • inverse problem
  • neural network
  • radial basis function

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