Journal of Intelligent and Robotic Systems

, Volume 16, Issue 2, pp 185–207 | Cite as

Selection of input variables for model identification of static nonlinear systems

  • Andreas Bastian
  • Jorge Gasós


System identification can be divided into structure and parameter identification. In most system-identification approaches the structure is presumed and only a parameter identification is performed to obtain the coefficients in the functional system. Yet, often there is little knowledge about the system structure. In such cases, the first step has to be the identification of the decisive input variables. In this paper a black-box input variable identification approach using feedforward neural networks is proposed.

Key words

Model identification static nonlinear systems feedforward neural networks structure identification 


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  1. Akaike, H.: 1974, A new look at statistical model indentification,IEEE Trans. Automat. Control 19, 716–723.Google Scholar
  2. Bastian, A.: 1994, An effecitive way to generate neural network structures for function approximation, LIFE Tech. Rep., MATHWARE & Softcomputing, in press.Google Scholar
  3. Bezdek, J. C: 1981,Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum, New York.Google Scholar
  4. Cybenko, G.: 1989, Approximation by superpositions of a sigmoidal function,Math. Ctrl., Signals, Sys. 2(4), 303–392.Google Scholar
  5. Friedman, J. H.: 1988, Multivariate adaptive regression splines, Tech. Rep., Department of Statistics, Standord University, CA.Google Scholar
  6. Fukuyama, Y. and Sugeno, M.: 1989, A new method of choosing the number of clusters for fuzzy c-means method, inProc. 5th Fuzzy System Symp., pp. 247–250 (in Japanese).Google Scholar
  7. Hecht-Nielsen, R.: 1981,Neurocomputing, Addison-Wesley, MA.Google Scholar
  8. Hornik, K., Stinchcomb, M., and White, H.: 1989, Multilayer feedforward networks are universal approximators,Neural Networks 2, 359–366.Google Scholar
  9. Ihara, J.: 1990, Group method of data handling towards a modeling of complex systems IV,System and control 24, 158–168 (in Japanese).Google Scholar
  10. Lin, C. T.: 1994, Neural Fuzzy Control Systems with Structure and Parameter Learning, World Scienctific.Google Scholar
  11. Narendra, K. S. and Parthasarathy, K.: 1990, Identification and control of dynamical systems using neural networks,IEEE Trans. on Neural Networks 1(1), 4–27.Google Scholar
  12. Ooyen, A. van and Nienhuis, B.: 1992, Improving the convergence of the back-propagation algorithm,Neural Networks 5, 465–471.Google Scholar
  13. Pirez, Y. M. and Sarkar, D.: 1993, Back-propagation with controlled oscillation of weights, inProc. IEEE Int. Conf. on NNÊ93, pp. 21–26.Google Scholar
  14. Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: 1986, Learning representation by back-propagation errors,Nature 232, 533–536.Google Scholar
  15. Sugeno, M. and Yasukawa, T.: 1993, A fuzzy-logic-based approach to qualitative modeling,IEEE Trans. on Fuzzy Systems 1(1), 7–31.Google Scholar
  16. Vogl, T. P., Mangis, J. K., Rigler, A. K., Zink, W. T. and Alkon, D. L.: 1988, Accelerating the convergence of the back-propagation method,Biol. Cybern. 59, 257–263.Google Scholar
  17. Yamada, T. and yabuta, T.: 1992, Neural network controller using autotuning method for nonlinear functions,IEEE Trans. on Neural Networks 3(4), 595–601.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Andreas Bastian
    • 1
  • Jorge Gasós
    • 1
  1. 1.Laboratory for International Fuzzy Engineering Research (LIFE)Yokohama 231Japan

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