X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/rairo15.git/blobdiff_plain/7adee4533e3b5462c283ce03d98ef5c440600e42..b60c35c87e498fa1be9cc75364cd069b18b06e9d:/intro.tex

diff --git a/intro.tex b/intro.tex
index cb79a58..b2cdd1c 100644
--- a/intro.tex
+++ b/intro.tex
@@ -1,15 +1,14 @@
 The exploitation of chaotic systems to generate pseudorandom sequences is
 an hot topic~\cite{915396,915385,5376454}. Such systems are fundamentally 
 chosen due to their unpredictable character and their sensibility to initial conditions.
+In most cases, these generators simply consist in iterating a chaotic function like 
+the logistic map~\cite{915396,915385} or the Arnold's one~\cite{5376454}\ldots
+It thus remains to find optimal parameters in such functions so that attractors are
+avoided, guaranteeing by doing so that generated numbers follow a uniform distribution.
+In order to check the quality of the produced outputs, it is usual to test the 
+PRNGs   (Pseudo-Random  Number   Generators) with statistical batteries like
+the so-called DieHARD~\cite{Marsaglia1996}, NIST~\cite{Nist10}, or TestU01~\cite{LEcuyerS07}.
 
-% Souvent   les   travaux  se   limitent   Ã    itÃ©rer   une  fonction   paramÃ©trÃ©e
-% \emph{chaotique}  comme la  fonction logistique~\cite{915396,915385},  ou encore
-% celle du  chat d'Arnold~\cite{5376454}\ldots Il  reste Ã  trouver  les paramÃ¨tres
-% optimaux permettant d'Ã©viter les attracteurs de telles fonctions et garantissant
-% que les sÃ©quences de nombres  produits suivent une loi de distribution uniforme.
-% Pour vÃ©rifier  la qualitÃ© des  sorties produites il  est usuel de  soumettre les
-% PRNG   (Pseudo-Random  Number   Generator)   Ã   des   tests  statistiques   tels
-% DieHARD~\cite{Marsaglia1996}, NIST~\cite{Nist10} et TestU01~\cite{LEcuyerS07}.
 % 
 % Dans son acception vulgarisÃ©e, 
 % la notion de chaos est souvent rÃ©duite Ã  celle de forte sensibilitÃ©
