Skip to main content
SpringerLink
Log in

Neural Systems with Numerically-Matched Input–Output Statistic: Variate Generation

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The aim of this paper is to present a neural system trained to exhibit matched input–output statistic for random samples generation. The learning procedure is based on a cardinal equation from statistics that suggests how to warp an available samples set of known probability density function into a samples set with desired probability distribution. The warping structure is realized by a fully-tunable neural system implemented as a look-up table. Learnability theorems are proven and discussed and the numerical features of the proposed methods are illustrated through computer-based experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Abramowitz C.A. Stegun (Eds) (1972) Handbook of Mathematical Functions with Formulas Graphs and Mathematical Tables Dover New York

    Google Scholar 

  2. Andrews G.E., Askey R., Roy R. Laguerre polynomials, Section 6.2, In: Special Functions. Cambridge, England: Cambridge University Press, 1999, pp. 282–293.

  3. P. L’Ecuyer (1998) Random number generation J. Banks (Eds) The Handbook of Simulation Wiley New York 93–137

    Google Scholar 

  4. Evans L.C.: Partial Differential Equations, Graduate Studies in Mathematics, 19, American Mathematical Society, 1998.

  5. S. Fiori (2000) ArticleTitleBlind signal processing by the adaptive activation function neurons Neural Networks 13 IssueID6 597–611 Occurrence Handle10.1016/S0893-6080(00)00044-7

    Article  Google Scholar 

  6. S. Fiori (2001) ArticleTitleProbability density function learning by unsupervised neurons Int. J. Neural Syst. 11 IssueID5 399–417

    Google Scholar 

  7. S. Fiori (July 2003) ArticleTitleNon-symmetric PDF estimation by artificial neurons: Application to statistical characterization of reinforced composites IEEE Trans. Neural Networks 14 IssueID4 959–962

    Google Scholar 

  8. Georgiou P. G., Kyriakakis C.: An alternative model for sound signals encountered in reverberant environments; robust maximum likelihood localization and parameter estimation based on a sub-Gaussian model, 113th Audio Engineering Society Convention, (October 2002), Los Angeles, CA, USA.

  9. G. Marsaglia (1985) A current view of random number generators L. Billard (Eds) Computer Science and Statistics: The Interface Elsevier Science Publishers B.V. Amsterdam 3–10

    Google Scholar 

  10. Morrison, F.: The Art of Modeling Dynamic Systems: Forecasting for Chaos, Randomness, and Determinism, Wiley-Interscience, January 1991.

  11. H. Ney (2000) ArticleTitleStochastic modeling: From pattern classification to speech recognition and translation IEEE International Conference on Pattern Recognition 3 3025

    Google Scholar 

  12. H. Niederreiter (1992) Random Number Generation and Quasi-Monte Carlo Methods SIAM Philadelphia

    Google Scholar 

  13. Papoulis A.: Probability and Statistics, Prentice Hall, 1996.

  14. A. Papoulis (1962) The Fourier Integral and Its Applications McGraw-Hill New York

    Google Scholar 

  15. B.D. Ripley (1990) ArticleTitleThoughts on pseudorandom number generators J. Comp. Appl. Math. 31 153–163 Occurrence Handle0701.65006

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni (DEIT). Facoltà di Ingegneria, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, 1-60131, Italy

    Simone Fiori

Authors
  1. Simone Fiori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simone Fiori.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fiori, S. Neural Systems with Numerically-Matched Input–Output Statistic: Variate Generation. Neural Process Lett 23, 143–170 (2006). https://doi.org/10.1007/s11063-005-4016-6

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-005-4016-6

Keywords

Search

Navigation