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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Wang, Z., Ulanowski, Z. & Kaye, P. On Solving the Inverse Scattering Problem with RBF Neural Networks: Noise-Free Case. Neural Computing & Applications 8, 177–186 (1999). https://doi.org/10.1007/s005210050019
Published:
Issue Date:
DOI: https://doi.org/10.1007/s005210050019