On solving the inverse scattering problem with RBF neural networks: Noise-free case

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.