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

Authors:
Simone Fiori
Affiliations:
Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni (DEIT). Facoltà di Ingegneria, Università Politecnica delle Marche, Ancona, Italy 1-60131
Venue:
Neural Processing Letters
Year:
2006

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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.