On Solving the Inverse Scattering Problem with RBF Neural Networks: Noise-Free Case

Abstract

Neural networks are successfully used to determine small particle properties from knowledge of the scattered light – an inverse light scattering problem. This type of problem is inherently difficult to solve as it is represented by a highly ill-posed function mapping. This paper presents a technique that solves the inverse light scattering problem for spheres using Radial Basis Function (RBF) neural networks. A two-stage network architecture is arranged to enhance network approximation capability. In addition, a new approach to computing basis function parameters with respect to the inverse scattering problem is demonstrated. The technique is evaluated for noise-free data through simulations, in which a minimum 99.06% approximation accuracy is achieved. A comparison is made between the least square and the orthogonal least square training methods.