Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm

Abstract EN

In a previous work (S.

Fiori, 2006), we proposed a random number generator based on a tunable non-linear neural system, whose learning rule is designed on the basis of a cardinal equation from statistics and whose implementation is based on look-up tables (LUTs).

The aim of the present manuscript is to improve the above-mentioned random number generation method by changing the learning principle, while retaining the efficient LUT-based implementation.

The new method proposed here proves easier to implement and relaxes some previous limitations.