Title :
An inverse-problem analysis of magnetopneumography with RBFN
Author :
Sued, Yoshiko ; Kotani, A. Makoto ; Aihara, Kazuyuki
Author_Institution :
Dept. of Electron. Eng., Tokyo Denki Univ., Japan
Abstract :
In this paper, we analyze the inverse problem of magnetopneumography (the magnetic field pattern produced from the lung) by radial basis function networks (RBFN) with smoothing ability. We show that radial basis function is naturally derived by calculation of the magnetic fields and RBFN composes of elements with such output function that is useful for the inverse problem of magnetopneumography.
Keywords :
feedforward neural nets; inverse problems; lung; magnetic fields; medical computing; pattern recognition; pneumodynamics; inverse-problem analysis; lung; magnetic field pattern; magnetopneumography; pneumodynamics; radial basis function networks; Humans; Inverse problems; Lungs; Magnetic analysis; Magnetic field measurement; Particle measurements; Radial basis function networks; Smoothing methods; X-ray detection; X-ray detectors;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.717051
