Journal of Electrical Engineering and information Science

대한전기학회 (The Korean Institute of Electrical Engineers)
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Statistical RBF Network with Applications to an Expert System for Characterizing Diabetes Mellitus

  • 발행 : 1998.06.01
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초록

The purposes of this study are to propose a network for the characterizing of the input data and to show how to design predictive neural net재가 expert system which doesn't need previous knowledge base. We derived this network from the radial basis function networks(RBFN), and named it as a statistical EBFN. The proposed network can replace the statistical methods for analyzing dynamic relations between target disease and other parameters in medical studies. We compared statistical RBFN with the probabilistic neural network(PNN) and fuzzy logic(FL). And we testified our method in the diabetes prediction and compared our method with the well-known multilayer perceptron(MLP) neural network one, and showed good performance of our network. At last, we developed the diabetes prediction expert system based on the proposed statistical RBFN without previous knowledge base. Not only the applicability of the characterizing of parameters related to diabetes and construction of the diabetes prediction expert system but also wide applicabilities has the proposed statistical RBFN to other similar problems.

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참고문헌

  1. Neural Computa. v.1 no.2 Fast learning in networks of locally-tuned processing units J.Moody;C.Darken
  2. Neural Networks S.Haykin
  3. IEEE Trans. Neural Networks v.7 no.6 Multiplication-free radial basis function network M.Heiss;S.Kampl
  4. IEEE Trans. Neural Networks v.7 no.5 Scale-based clustering using the radial basis function network S.V.Chakravarthy;J.Ghosh
  5. IEEE Trans. Neural Networks v.7 no.6 On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator L.Behera;M.Gopal;S.Chaudhury
  6. IEEE Trans. Neural Networks v.7 no.5 Human expression recognition from motion using a radial basis function network architecture M.Rosenblum;Y.Yacoob;L.S.Davis
  7. IEEE Trans. Neural Networks v.7 no.6 Median radial basis funtion neural network A.G.Bors;I.Pitas
  8. The Handbook of Artificial Intelligence v.I A.Barr;E.A.Feigenbaum
  9. IEEE Trans. Neural Networks v.7 no.3 Entropy maximization networks: An application to breast cancer prognosis P.L.Choong;C.J.S.deSilva;H.J.S.Dawkins;G.F.Sterrett
  10. IEEE Trans. Neural Networks v.7 no.6 Learning capacity and sample complexity on expert networks L.M.Fu
  11. Fuzzy Expert Systems A.Kandel
  12. IEEE Trans. Fuzzy Systems v.4 no.3 Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application D.Cayrac;D.Dubois;H.Prade
  13. IEEE Trans. Neural Networks v.7 no.2 A modular neural network approach to fault diagnosis C.Rodriguez;S.Rementeria;J.I.Martin;A.Lafuente;J.Muguerza;J.Perez
  14. Advances in Numerical Analysis v.3 The theory radial basis function approximation in 1990 M.J.D.Powell;W.Light(Ed.)
  15. Neural Computa v.2 Layered neural networks with Gaussian hidden units as universal approximation E.J.Hartman;J.D.Keeler;J.M.Kowalski
  16. Neural Computa v.3 Universal approximation using radial basis function Networks J.Park;I.W.Sandberg
  17. Neural Computa. v.5 Approximation and radial basis function networks J.Park;I.W.Sandberg
  18. Adaptive Filter Theory S.Haykin
  19. IEEE Trans. Neural Networks v.7 no.4 Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction B.A.Whitehead;T.D.Choate
  20. Complex Syst. v.2 Multivariable function interpolation and adaptive networks D.S.Broomhead;D.Lowe
  21. Neural Networks v.5 On the training of radial basis function classifier T.Musavi;W.Ahmed;K.H.Chan;K.B.Faris;D.M.Hummels
  22. IEEE Trans. Neural Networks v.2 no.2 Orthogonal least squares learning algorithm for radial basis function networks Chen,C.F.N.Cowan;P.M.Grant
  23. Proceedings of the IEEE v.78 no.9 Networks for approximation and learning T.Poggio;R.Girosi
  24. Neural Networks v.3 Probabilistic neural networks F.Specht
  25. Fuzzy Sets and Systems v.1 Fuzzy sets as basis for theory of possibility L.A.Zadeh
  26. Fuzzy Expert System Tools M.Schneider;A.Kandel;G.Langholz;G.Chew
  27. IEEE Trans. Neural Networks v.7 no.3 Extending the functional equivalence of radial basis function networks and fuzzy inference systems K.J.Hunt;R.Haas;R.M.Smith
  28. IEEE Trans. Neural Networks v.4 no.1 Functional equivalence between radial basis function networks and fuzzy inference systems J.S.R.Jang;C.T.Sun