Abstract
A new approach suitable for solving inverse problems in multiangle
light scattering is presented. The method takes advantage of
multidimensional function approximation capability of radial basis
function neural networks. An algorithm for training the networks is
described in detail. It is shown that the radius and refractive
index of homogeneous 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.
© 1998 Optical Society of America
Full Article |
PDF Article
More Like This
References
You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Tables (2)
You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Equations (13)
You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Metrics
You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription