Advertisement

Feedback GMDH-Type Neural Network Algorithm Using Prediction Error Criterion Defined as AIC

  • Tadashi Kondo
  • Junji Ueno
  • Shoichiro Takao
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 16)

Abstract

In this study, a feedback Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as AIC, is proposed. In this algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as sigmoid function type neural network, radial basis function (RBF) type neural network and polynomial type neural network. Furthermore, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and useful input variables are automatically selected so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC). Feedback GMDH-type neural network has a feedback loop and the complexity of the neural network increases gradually using feedback loop calculations so as to fit the complexity of the nonlinear system. This algorithm is applied to identification problem of the complex nonlinear system.

Keywords

Neural network GMDH AIC Self-organization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kondo, T., Ueno, J.: Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neurons. Artificial Life and Robotics 16(3), 301–306 (2011)CrossRefGoogle Scholar
  2. 2.
    Kondo, T., Ueno, J.: Medical image recognition of abdominal multi-organs by RBF GMDH-type neural network. International Journal of Innovative Computing, Information and Control 5(1), 225–240 (2009)Google Scholar
  3. 3.
    Farlow, S.J. (ed.): Self-organizing methods in modeling, GMDH-type algorithm. Marcel Dekker, Inc., New York (1984)Google Scholar
  4. 4.
    Ivakhnenko, A.G.: Heuristic self-organization in problems of engineering cybernetics. Automatica 6(2), 207–219 (1970)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tamura, H., Kondo, T.: Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction. Int. J. System Sci. 11(9), 1095–1111 (1980)CrossRefGoogle Scholar
  6. 6.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automatic Control AC-19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Draper, N.R., Smith, H.: Applied Regression Analysis. John Wiley and Sons, New York (1981)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tadashi Kondo
    • 1
  • Junji Ueno
    • 1
  • Shoichiro Takao
    • 1
  1. 1.Graduate School of Health SciencesThe University of TokushimaTokushimaJapan

Personalised recommendations