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

        Author
        Wang, Z
        Ulanowski, Z
        Kaye, Paul H.
        Attention
        2299/7457
        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.
        Publication date
        1999
        Published in
        Neural Computing and Applications
        Published version
        https://doi.org/10.1007/s005210050019
        Other links
        http://hdl.handle.net/2299/7457
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