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.
+chosen due to their unpredictable character and their sensitiveness 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
PRNGs (Pseudo-Random Number Generators) with statistical batteries like
the so-called DieHARD~\cite{Marsaglia1996}, NIST~\cite{Nist10}, or TestU01~\cite{LEcuyerS07}.
-%
-% Dans son acception vulgarisée,
-% la notion de chaos est souvent réduite à celle de forte sensibilité
-% aux conditions initiales (le fameux \og \emph{effet papillon}\fg{}):
-% une fonction continue $k$ définie sur un espace métrique
-% est dite \emph{fortement sensible aux conditions initiales} si pour tout
-% point $x$ et pour toute valeur positive $\epsilon$
-% il est possible de trouver un point $y$, arbitrairement proche
-% de $x$, et un entier $t$ tels que la distance entre les
-% $t^{\textrm{ièmes}}$ itérés de $x$ et de $y$
-% -- notés $k^t(x)$ et $k^t(y)$
-% -- est supérieure à $\epsilon$.
+In its general understanding, the chaos notion is often reduced to the strong
+sensitiveness to the initial conditions (the well known ``butterfly effect''):
+a continuous function $k$ defined on a metrical space is said
+\emph{strongly sensitive to the initial conditions} if for all point
+$x$ and all positive value $\epsilon$, it is possible to find another
+point $y$, as close as possible to $x$, and an integer $t$ such that the distance
+between the $t$-th iterates of $x$ and $y$, denoted by $k^t(x)$ and $k^t(y)$,
+are larger than $\epsilon$.
% Cependant, dans sa définition du chaos,
% Devaney~\cite{Devaney} impose à la fonction chaotique deux autres propriétés
% appelées \emph{transitivité} et \emph{régularité},
