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

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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.

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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

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  • DOI: https://doi.org/10.1007/s005210050019

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