JACIII Vol.11 No.3 pp. 312-318
doi: 10.20965/jaciii.2007.p0312


Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve

Tadashi Kondo and Junji Ueno

School of Health Sciences, The University of Tokushima, 3-l8-15 Kuramoto-cho, Tokushima 770-8509, Japan

April 17, 2006
September 6, 2006
March 20, 2007
GMDH, neural network, identification, X-ray film

The logistic Group Method of Data Handing (GMDH)-type neural network identifying a complex nonlinear system we propose is automatically organized using heuristic self-organization that is basic to GMDH algorithm. In this neural network, structural parameters such as the number of layers, the number of neurons per layer, useful input variables, and optimum neuron architectures are automatically determined using a prediction error criterion defined as Akaike’s Information Criterion (AIC) to produce an optimum neural network architecture suiting the complexity of the nonlinear system. In applying this neural network to the identification problem of the X-ray film characteristic curve, we found that modeling with such a neural network is more accurate than applying multiple regression analysis, a conventional neural network, and GMDH algorithm.

Cite this article as:
Tadashi Kondo and Junji Ueno, “Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.3, pp. 312-318, 2007.
Data files:
  1. [1] T. Kondo, “GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem,” Proc. of the 37th SICE Annual Conference, International Session Paper, pp. 1143-1148, 1998.
  2. [2] T. Kondo, “The learning algorithms of the GMDH neural network and their application to the medical image recognition,” Proc. of the 37th SICE Annual Conference, International Session Paper, pp. 1109-1114, 1998.
  3. [3] T. Kondo, “GMDH type Neural Network Algorithm Identifying a Network Structure with the Heuristic Self-Organization Method,” Trans. ISCIE, Vol.11, No.4, pp. 198-207, 1998.
  4. [4] D. T. Pham and X. Liu, “Neural networks for identification, Prediction and Control,” London, Springer-Verlag, 1995.
  5. [5] R. H. Nielsen, “Neurocomputing,” Addison-Wesley, 1990.
  6. [6] J. M. Zurada, “Introduction to Artificial Neural Systems,” Boston, PWS, 1992.
  7. [7] A. G. Ivakhnenko, “Heuristic self-organization in problems of engineering cybernetics,” Automatica, Vol.6, No.2, pp. 207-219, 1970.
  8. [8] S. J. Farlow (Ed.), “Self-organizing Methods in Modeling, GMDHtype Algorithms,” New York, Marcel Dekker, Inc., 1984.
  9. [9] A. G. Ivakhnenko, G. A. lvakhnenko, and J. A. Muller, “Selforganization of the neural networks with active neurons,” Pattern Recognition and Image Analysis, Vol.4, No.2, pp. 177-188, 1994.
  10. [10] A. G. Ivakhnenko and J. A. Muller, “Self-organization of nets of active neurons,” SAMS, Vol.20, pp. 93-106, 1995.
  11. [11] H. Tamura and T. Kondo, “Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction,” Int. J. Systems Sci., Vol.11, No.9, pp. 1095-1111, 1980.
  12. [12] H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automatic Control, Vol.AC-19, No.6, pp. 716-723, 1974.
  13. [13] N. R. Draper and H. Smith, “Applied Regression Analysis,” New York, John Wiley and Sons, 1981.
  14. [14] T. Kondo, “Identification of the X-ray film characteristic curve by using the neural network,” Bull. Sch. Med. Sci. Univ. Tokushima, Vol.7, pp. 9-16, 1997.

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