Analytical results on pseudo-polynomial functional-link neural units for blind density shaping

Author

Fiori, Simone ; Burrascano, Pietro

Author_Institution

Dept. of Ind. Eng., Perugia Univ., Italy

Volume

2

fYear

1999

fDate

36342

Firstpage

1117

Abstract

We deal with the problem of approximating the cumulative distribution of a quasi-stationary signal by means of a parametric function. We present an algorithm based on a quasi-polynomial adaptive transformation, and briefly recall an existing closely related technique, the `mixture of densities´ technique. Then through numerical simulations both on synthetic and real-world data we compare their performances in terms of convergence speed and computational complexity

Keywords

computational complexity; convergence; digital simulation; learning (artificial intelligence); maximum entropy methods; neural nets; polynomials; probability; signal processing; blind density shaping; convergence speed; cumulative distribution; mixture of densities technique; parametric function; pseudo-polynomial functional-link neural units; quasi-polynomial adaptive transformation; quasi-stationary signal; real-world data; synthetic data; Computational complexity; Convergence of numerical methods; Distribution functions; Entropy; Humans; Industrial engineering; Numerical simulation; Parameter estimation; Polynomials; Probability density function;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831113

Filename

831113