A method to retrieve the radius and the relative refractive index of spherical homogeneous nonabsorbing particles by multiangle scattering is proposed. It is based on the formation of noise-resistant functionals of the scattered intensity, which are invariant with respect to the linear homogeneous transformations of an intensity-based signal and approximation of the retrieved parameters' dependence on the functionals by a feed-forward neural network. The neural network was trained by minimization of the mean squared relative error in the range of particle radii from 0.6 mkm up to 13.6 mkm and relative refractive index from 1.015 up to 1.28. In comparison with training on a minimum of the mean squared error, this method enables one to increase the accuracy of the radius retrieval in the range of radii from 0.6 to 2 microm and refractive index in the range from 1.015 to 1.1. The values of intensity of light scattered in the interval of angles 10 degrees-60 degrees are used as input data. If the measurement error is 20%, the mean errors of the radius and relative refractive index are 0.8% and 7%, respectively. The results obtained by the proposed method and by the trial and error method with published experimental data (measured with a scanning flow cytometer) are compared. The maximal difference in the retrieval results of radius and the relative refractive index of particles obtained by both methods is under 5%.

A method for evaluating the size of optically soft spheroidal particles by use of the angular structure of scattered light is proposed. It is based on the use of multilevel neural networks with a linear activation function. The retrieval errors of radius R of the equivolume sphere and aspect ratio e are investigated. The ranges of the size of R, e, and the refractive index are 0.3-1.51 microns, 0.2-1, and 1.01-1.02, respectively. The retrieval errors of the equivolume radius and aspect ratio are 0.004 micron and 0.02, respectively, for a three-level neural network (at a precisely measured angular distribution of scattered light). The retrieval errors of R and e for a one-level neural network are 2-5 times greater. The errors for a multilevel neural network increase faster than those for a single-level network.

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