World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

PREDICTION OF NONLINEAR SYSTEM IN OPTICS USING GENETIC PROGRAMMING

    https://doi.org/10.1142/S0129183107009777Cited by:4 (Source: Crossref)

    It is difficult to predict the dynamics of systems which are nonlinear and whose characteristic is unknown. In order to build a model of the system from input and output data without any knowledge about the system, we try automatically to build prediction model by Genetic Programming (GP).

    GP has been used to discover the function that describes nonlinear system to study the effect of wavelength and temperature on the refractive index of the fiber core. The predicted distribution from the GP based model is compared with the experimental data. The discovered function of the GP model has proved to be an excellent match to the experimental data.

    References

    • K. J. Astrom and P. Eykhoff, Automatica 7, (1971). Google Scholar
    • X. C. Jian and J. L. Zheng, Acta Electronica Sinica 30, 76 (2002). Web of ScienceGoogle Scholar
    • I. Yoshihara and S. Sato, Nonlinear model building method with GA and GMDH, IPSJ, AI-105, 1–6 (1996) (in Japanese); Advances in Genetic Programming, ed. K. E. Kinnear, Jr. (MIT Press, 1994) . Google Scholar
    • H.   Iba , H.   deGaris and T.   Sato , Recombination guidance for numerical genetic programming , Proc. of 2nd International Conference on Evolutionary Computation ( IEEE Press , 1995 ) . Google Scholar
    • J. R.   Koza , Genetic Programming: On the Programming of Computers by Means of Natural Selection ( MIT Press , Cambridge, MA , 1992 ) . Google Scholar
    • W.   Banzhaf et al. , Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications ( dpunkt.verlag and Morgan Kaufmann , 1998 ) . Google Scholar
    • J. R.   Koza et al. , Genetic Programming IV: Routine Human-Competitive Machine Intelligence ( Kluwer Academic Publishers , 2003 ) . Google Scholar
    • J. M. Link, Nucl. Instrum. Meth. A 551, 504 (2005), hep-ex/0503007. Crossref, Web of Science, ADSGoogle Scholar
    • G. C. Bhar, Applied Optics 15, 305 (1976). ADSGoogle Scholar
    • M. Medhatet al., J. Optics A: Pure Applied Optics 4, 174 (2002), DOI: 10.1088/1464-4258/4/2/309. Crossref, Web of Science, ADSGoogle Scholar
    • J. R.   Koza , S. H.   Al-Sakra and W. J.   Lee , Automated re-invention of six patented optica lens systems using genetic programming , Genetic and Evolutionary Computation Conference (GECCO) '05 ( 2005 ) . Google Scholar
    You currently do not have access to the full text article.

    Recommend the journal to your library today!