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In many fields of science the remote sensing often presents the only way
to investigate scattering objects.
It allows estimating different microphysical properties of
scatterers (size, complex refractive index, shape, etc.) from
measured characteristics of the scattered radiation.
This inverse scattering problem is usually solved by fitting
the observed results with those obtained from solutions of
the direct problem.
The complexity of the direct problem depends on its input parameters
characterizing the shape, size and structure of the scattering particle.
Sometimes to reduce the number of these parameters and to simplify the
calculations, regular shape (spherical, spheroidal, etc.)
scatterer models are applied.
However, many particles of natural and artificial origin
are aggregates (clusters) of subparticles
well resembling fractal-like objects
(for instance, cometary and interplanetary dust grains [1]).
As such compound particles strongly distinguish
in shape and structure from the regular shape models,
their optical properties appreciably differ
from those of isolated model particles [2].
Calculation of scattered radiation characteristics
for fractal clusters is a time consuming procedure.
Moreover, for interpretation of remote
sensing results such calculations must be carried out in a wide range of
the size parameters, complex refractive indexes and parameters,
characterizing the cluster structure.
Therefore, to solve the inverse scattering problem
a database with detailed information on
the scattering properties of fractal cluster is required.
We produced such a database using an artificial neural network.
This network when being properly trained allows one well to determine
required data.
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2003-04-11