Computed tomography by neuro-fuzzy inversion

Moody and Darken (1989) proposed a network architecture which uses a single internal layer of locally-tuned processing units to learn real-valued function approximations. The network can be reinterpreted as both neural networks and fuzzy rules. Hence, we call it neuro-fuzzy and proposes method of geophysical computerized tomography. The line integrals of Gaussian radial basis functions can be obtained in a simple manner and the spatial distribution is calculated from the line integrals along rays in a plane. With this method, detailed pictures of the spatial distribution of attenuation or propagation velocity can be reconstructed from a small number of measured data.