Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm | Zendy

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Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm

Author(s) -

Simone Fiori

Publication year - 2008

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2008/426080

Subject(s) - random variate , computer science , dual (grammatical number) , algorithm , random number generation , generator (circuit theory) , lookup table , artificial intelligence , basis (linear algebra) , artificial neural network , machine learning , mathematics , random variable , statistics , power (physics) , art , physics , geometry , literature , quantum mechanics , programming language

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.

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