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Asymmetric variate generation via a parameterless dual neural learning algorithm.

Fiori S - Comput Intell Neurosci (2008)

Bottom Line: 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 new method proposed here proves easier to implement and relaxes some previous limitations.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni (DEIT), Università Politecnica delle Marche Via Brecce Bianche, Ancona I-60131, Italy. fiori@deit.univpm.it

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Result of dual neuralsystem adaptation with Gaussian input andGamma output.
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fig5: Result of dual neuralsystem adaptation with Gaussian input andGamma output.

Mentions: The numerical results presented below pertain tovalues α = 0.8 and β = 4. The interval of interest for the output variable is set to 𝓎 = [−2 2]. The total number of generated output samples amounts to Q = 68355. The number of points in which the function g(⋅) is computed is N = 1500. The results obtained by running the learning algorithm (7) are shown in Figure 5. The values of the index Δgn show that the fixed-point algorithm may be safely stopped after 5 iterations again. Figure 5 shows the histogram estimates (with 50 bins) of the generated Gaussian data and of the Gamma-distributed output.


Asymmetric variate generation via a parameterless dual neural learning algorithm.

Fiori S - Comput Intell Neurosci (2008)

Result of dual neuralsystem adaptation with Gaussian input andGamma output.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2376097&req=5

fig5: Result of dual neuralsystem adaptation with Gaussian input andGamma output.
Mentions: The numerical results presented below pertain tovalues α = 0.8 and β = 4. The interval of interest for the output variable is set to 𝓎 = [−2 2]. The total number of generated output samples amounts to Q = 68355. The number of points in which the function g(⋅) is computed is N = 1500. The results obtained by running the learning algorithm (7) are shown in Figure 5. The values of the index Δgn show that the fixed-point algorithm may be safely stopped after 5 iterations again. Figure 5 shows the histogram estimates (with 50 bins) of the generated Gaussian data and of the Gamma-distributed output.

Bottom Line: 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 new method proposed here proves easier to implement and relaxes some previous limitations.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni (DEIT), Università Politecnica delle Marche Via Brecce Bianche, Ancona I-60131, Italy. fiori@deit.univpm.it

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus