corps fini

[chaos1.git] / main.tex

diff --git a/main.tex b/main.tex
index 717ae3e688d7b36d00dcd58280383073825e8c46..cee88400c5a74d8f73babf97e75ca1fb7f6a49aa 100644 (file)
--- a/main.tex
+++ b/main.tex
@@ -58,6 +58,11 @@ IUT de Belfort-Montb\'eliard, BP 527, \\
 \date{\today}
 
 \newcommand{\CG}[1]{\begin{color}{red}\textit{#1}\end{color}}
 \date{\today}
 
 \newcommand{\CG}[1]{\begin{color}{red}\textit{#1}\end{color}}

+\newcommand{\JFC}[1]{\begin{color}{blue}\textit{#1}\end{color}}
+
+
+
+

 
 \begin{abstract}
 %% Text of abstract
 
 \begin{abstract}
 %% Text of abstract


@@ -65,14 +70,15 @@ Many  research works  deal with  chaotic neural  networks  for various
 fields  of application. Unfortunately,  up to  now these  networks are
 usually  claimed to be  chaotic without  any mathematical  proof.  The
 purpose of this paper is to establish, based on a rigorous theoretical
 fields  of application. Unfortunately,  up to  now these  networks are
 usually  claimed to be  chaotic without  any mathematical  proof.  The
 purpose of this paper is to establish, based on a rigorous theoretical

-framework, an equivalence between  chaotic iterations according to the
-Devaney's  formulation  of chaos  and  a  particular  class of  neural
+framework, an equivalence between  chaotic iterations according to 
+Devaney  and  a  particular  class of  neural

 networks. On the one hand we show  how to build such a network, on the
 other  hand we provide  a method  to check  if a  neural network  is a
 chaotic one.  Finally, the ability of classical feedforward multilayer
 perceptrons to  learn sets of  data obtained from a  chaotic dynamical
 system is regarded.  Various  Boolean functions are iterated on finite
 networks. On the one hand we show  how to build such a network, on the
 other  hand we provide  a method  to check  if a  neural network  is a
 chaotic one.  Finally, the ability of classical feedforward multilayer
 perceptrons to  learn sets of  data obtained from a  chaotic dynamical
 system is regarded.  Various  Boolean functions are iterated on finite

-states, some  of them  are proven to  be chaotic  as it is  defined by
+states. Iterations of some of them  are proven to  be chaotic 
+ as it is  defined by

 Devaney.  In that context, important differences occur in the training
 process,  establishing  with  various  neural  networks  that  chaotic
 behaviors are far more difficult to learn.
 Devaney.  In that context, important differences occur in the training
 process,  establishing  with  various  neural  networks  that  chaotic
 behaviors are far more difficult to learn.


@@ -85,6 +91,7 @@ behaviors are far more difficult to learn.
 \maketitle
 
 \begin{quotation}
 \maketitle
 
 \begin{quotation}

+

 Chaotic neural  networks have received a  lot of attention  due to the
 appealing   properties  of   deterministic   chaos  (unpredictability,
 sensitivity,  and so  on). However,  such networks  are  often claimed
 Chaotic neural  networks have received a  lot of attention  due to the
 appealing   properties  of   deterministic   chaos  (unpredictability,
 sensitivity,  and so  on). However,  such networks  are  often claimed


@@ -105,6 +112,9 @@ is far more difficult than non chaotic behaviors.
 \section{Introduction}
 \label{S1}
 
 \section{Introduction}
 \label{S1}
 

+REVOIR TOUT L'INTRO et l'ABSTRACT en fonction d'asynchrone, chaotic
+
+

 Several research  works have proposed  or run chaotic  neural networks
 these last  years.  The complex dynamics  of such a  networks leads to
 various       potential      application       areas:      associative
 Several research  works have proposed  or run chaotic  neural networks
 these last  years.  The complex dynamics  of such a  networks leads to
 various       potential      application       areas:      associative


@@ -129,7 +139,9 @@ which  is usually  assessed through  the computation  of  the Lyapunov
 exponent.  An alternative approach  is to consider a well-known neural
 network architecture: the  MultiLayer Perceptron (MLP). These networks
 are  suitable to model  nonlinear relationships  between data,  due to
 exponent.  An alternative approach  is to consider a well-known neural
 network architecture: the  MultiLayer Perceptron (MLP). These networks
 are  suitable to model  nonlinear relationships  between data,  due to

-their universal approximator capacity. Thus, this kind of networks can
+their universal approximator capacity.
+\JFC{Michel, peux-tu donner une ref la dessus}
+Thus, this kind of networks can

 be trained to model a physical  phenomenon known to be chaotic such as
 Chua's circuit  \cite{dalkiran10}.  Sometimes, a  neural network which
 is build by combining  transfer functions and initial conditions that are both
 be trained to model a physical  phenomenon known to be chaotic such as
 Chua's circuit  \cite{dalkiran10}.  Sometimes, a  neural network which
 is build by combining  transfer functions and initial conditions that are both


@@ -138,13 +150,13 @@ chaotic,      is      itself      claimed      to      be      chaotic
 
 What all of these chaotic neural  networks have in common is that they
 are claimed to be chaotic  despite a lack of any rigorous mathematical
 
 What all of these chaotic neural  networks have in common is that they
 are claimed to be chaotic  despite a lack of any rigorous mathematical

-proof.   The first contribution of  this paper  is  to fill  this  gap, using  a
-theoretical  framework  based on  the  Devaney's  definition of  chaos
+proof.   The first contribution of  this paper  is  to fill  this  gap, 
+using  a theoretical  framework  based on  the  Devaney's  definition of  chaos

 \cite{Devaney}.   This  mathematical  theory  of chaos  provides  both
 qualitative and quantitative tools to evaluate the complex behavior of
 a  dynamical  system:  ergodicity,   expansivity,  and  so  on.   More
 precisely, in  this paper,  which is an  extension of a  previous work
 \cite{Devaney}.   This  mathematical  theory  of chaos  provides  both
 qualitative and quantitative tools to evaluate the complex behavior of
 a  dynamical  system:  ergodicity,   expansivity,  and  so  on.   More
 precisely, in  this paper,  which is an  extension of a  previous work

-\cite{bgs11:ip},   we  establish   the  equivalence  between   chaotic
+\cite{bgs11:ip},   we  establish   the  equivalence  between asynchronous

 iterations and  a class of globally  recurrent MLP.
 The investigation the  converse problem is the second contribution: 
 we indeed study the  ability for
 iterations and  a class of globally  recurrent MLP.
 The investigation the  converse problem is the second contribution: 
 we indeed study the  ability for


@@ -152,8 +164,8 @@ classical MultiLayer Perceptrons  to learn a particular family of
 discrete  chaotic  dynamical  systems.   This family,  called  chaotic
 iterations, is defined by a  Boolean vector, an update function, and a
 sequence giving  which component  to update at  each iteration.   It has
 discrete  chaotic  dynamical  systems.   This family,  called  chaotic
 iterations, is defined by a  Boolean vector, an update function, and a
 sequence giving  which component  to update at  each iteration.   It has

-been  previously established  that such  dynamical systems  can behave
-chaotically, as it is defined by Devaney, when the chosen function has
+been  previously established  that such  dynamical systems is 
+chaotically iterated (as it is defined by Devaney) when the chosen function has

 a  strongly connected  iterations graph.   In this  document,  we 
 experiment several MLPs and try to learn some iterations of this kind.
 We  show that non-chaotic iterations can be learned, whereas it is
 a  strongly connected  iterations graph.   In this  document,  we 
 experiment several MLPs and try to learn some iterations of this kind.
 We  show that non-chaotic iterations can be learned, whereas it is


@@ -163,7 +175,7 @@ such  that artificial  neural networks  should not  be applied
 due to their inability to  learn chaotic behaviors in this context.
 
 The remainder of this research  work is organized as follows. The next
 due to their inability to  learn chaotic behaviors in this context.
 
 The remainder of this research  work is organized as follows. The next

-section is devoted  to the basics of chaotic  iterations and Devaney's
+section is devoted  to the basics of Devaney's

 chaos.   Section~\ref{S2} formally  describes  how to  build a  neural
 network  that operates  chaotically.  Section~\ref{S3} is 
 devoted to the dual case of  checking whether an existing neural network
 chaos.   Section~\ref{S2} formally  describes  how to  build a  neural
 network  that operates  chaotically.  Section~\ref{S3} is 
 devoted to the dual case of  checking whether an existing neural network


@@ -180,18 +192,21 @@ system.  Prediction success rates are  given and discussed for the two
 sets.  The paper ends with a conclusion section where our contribution
 is summed up and intended future work is exposed.
 
 sets.  The paper ends with a conclusion section where our contribution
 is summed up and intended future work is exposed.
 

-\section{Link between Chaotic Iterations and Devaney's Chaos}
+\section{Chaotic Iterations according to  Devaney}

 
 In this section, the well-established notion of Devaney's mathematical
 chaos is  firstly recalled.  Preservation of  the unpredictability of
 such dynamical  system when implemented  on a computer is  obtained by
 
 In this section, the well-established notion of Devaney's mathematical
 chaos is  firstly recalled.  Preservation of  the unpredictability of
 such dynamical  system when implemented  on a computer is  obtained by

-using  some discrete iterations  called ``chaotic  iterations'', which
-are thus introduced.  The result establishing the link between chaotic
+using  some discrete iterations  called ``asynchronous  iterations'', which
+are thus introduced.  The result establishing the link between such

 iterations and Devaney's chaos is  finally presented at the end of this
 section.
 
 In what follows and for  any function $f$, $f^n$ means the composition
 iterations and Devaney's chaos is  finally presented at the end of this
 section.
 
 In what follows and for  any function $f$, $f^n$ means the composition

-$f \circ f \circ \hdots \circ f$ ($n$ times).
+$f \circ f \circ \hdots \circ f$ ($n$ times) and an \emph{iteration}
+of a \emph{dynamical system} is the step that consists in
+updating the global state $x^t$ at time $t$ with respect to a function $f$ 
+s.t. $x^{t+1} = f(x^t)$.    

 
 \subsection{Devaney's chaotic dynamical systems}
 
 
 \subsection{Devaney's chaotic dynamical systems}
 


@@ -227,78 +242,65 @@ point dependence on initial conditions.
 
 Topological  transitivity   is  checked  when,  for   any  point,  any
 neighborhood of its future evolution eventually overlap with any other
 
 Topological  transitivity   is  checked  when,  for   any  point,  any
 neighborhood of its future evolution eventually overlap with any other

-given region. More precisely,
-
-\begin{definition} \label{def2}
-A continuous function $f$  on a topological space $(\mathcal{X},\tau)$
-is defined  to be  {\bf topologically transitive}  if for any  pair of
-open  sets $U$,  $V  \in  \mathcal{X}$ there  exists  
-$k \in
-\mathds{N}^{\ast}$
- such  that
-$f^k(U) \cap V \neq \emptyset$.
-\end{definition}
-
-This property  implies that a  dynamical system cannot be  broken into
-simpler  subsystems.  
+given region.  This property  implies that a  dynamical system
+cannot be  broken into simpler  subsystems.  

 Intuitively, its complexity does not allow any simplification.
 On  the contrary,  a dense set  of periodic points  is an
 element of regularity that a chaotic dynamical system has to exhibit.
 
 Intuitively, its complexity does not allow any simplification.
 On  the contrary,  a dense set  of periodic points  is an
 element of regularity that a chaotic dynamical system has to exhibit.
 

-\begin{definition} \label{def3}
-A point $x$ is called a  {\bf periodic point} for $f$ of period~$n \in
-\mathds{N}^{\ast}$ if $f^{n}(x)=x$.
-\end{definition}
-
-\begin{definition} \label{def4}
-$f$ is said to be {\bf  regular} on $(\mathcal{X},\tau)$ if the set of
-  periodic points  for $f$ is  dense in $\mathcal{X}$ ( for any $x \in
+However, chaos need some regularity to ``counteracts'' 
+the  effects of  transitivity.
+%\begin{definition} \label{def3}
+We recall that a  point $x$ is  {\emph{periodic point}} for $f$ of 
+period~$n \in \mathds{N}^{\ast}$ if $f^{n}(x)=x$.
+%\end{definition}
+Then, the map 
+%\begin{definition} \label{def4}
+$f$ is {\emph{ regular}} on $(\mathcal{X},\tau)$ if the set of
+  periodic points  for $f$ is  dense in $\mathcal{X}$ (for any $x \in

   \mathcal{X}$, we can find at least  one periodic point in any of its
   neighborhood).
   \mathcal{X}$, we can find at least  one periodic point in any of its
   neighborhood).

-\end{definition}
+%\end{definition}
+  Thus,
+  due to these two properties, two points close to each other can behave
+  in a completely different  manner, leading to unpredictability for the
+  whole system.

 
 

-This  regularity ``counteracts'' the  effects of  transitivity.  Thus,
-due to these two properties, two points close to each other can behave
-in a completely different  manner, leading to unpredictability for the
-whole system. Then,
-
-\begin{definition} \label{sensitivity}
-$f$  has {\bf  sensitive dependence  on initial  conditions}  if there
-  exists $\delta  >0$ such  that, for any  $x\in \mathcal{X}$  and any
-  neighborhood $V$ of $x$, there exist  $y\in V$ and $n > 0$ such that
-  $d\left(f^{n}(x), f^{n}(y)\right) >\delta  $. The value $\delta$ is called the
-  {\bf constant of sensitivity} of $f$.
-\end{definition}
+Let we recall that $f$  
+has {\emph{  sensitive dependence  on initial  conditions}}  if there
+exists $\delta  >0$ such  that, for any  $x\in \mathcal{X}$  and any
+neighborhood $V$ of $x$, there exist  $y\in V$ and $n > 0$ such that
+$d\left(f^{n}(x), f^{n}(y)\right) >\delta  $. The value $\delta$ is called the
+  {\emph{constant of sensitivity}} of $f$.

 
 

-Finally,
+Finally, The dynamical system that iterates $f$ is {\emph{ chaotic according to Devaney}} on $(\mathcal{X},\tau)$ if $f$  is   regular,  topologically  transitive,   
+and  has  sensitive dependence to its initial conditions.
+In what follows, iterations are said to be \emph{chaotic according Devaney}
+when corresponding dynamical system  is chaotic according Devaney.

 
 

-\begin{definition} \label{def5}
-$f$ is  {\bf chaotic according to Devaney}  on $(\mathcal{X},\tau)$ if
-  $f$  is   regular,  topologically  transitive,   and  has  sensitive
-  dependence to its initial conditions.
-\end{definition}

 
 

-Let us notice that for a  metric space the last condition follows from
-the two first ones~\cite{Banks92}.
+%Let us notice that for a  metric space the last condition follows from
+%the two first ones~\cite{Banks92}.

 
 

-\subsection{Chaotic Iterations}
+\subsection{Asynchronous Iterations}

 
 

-This section  presents some basics on  topological chaotic iterations.
+%This section  presents some basics on  topological chaotic iterations.

 Let us  firstly discuss about the  domain of iteration.  As  far as we
 Let us  firstly discuss about the  domain of iteration.  As  far as we

-know, no result rules that the chaotic behavior of a function that has
-been theoretically proven on  $\R$ remains valid on the floating-point
+know, no result rules that the chaotic behavior of a dynamical system 
+that has been theoretically proven on  $\R$ remains valid on the
+floating-point

 numbers, which is  the implementation domain.  Thus, to  avoid loss of
 chaos this  work presents an  alternative, that is to  iterate Boolean
 maps:  results that  are  theoretically obtained  in  that domain  are
 preserved in implementations.
 
 Let us denote by $\llbracket  a ; b \rrbracket$ the following interval
 numbers, which is  the implementation domain.  Thus, to  avoid loss of
 chaos this  work presents an  alternative, that is to  iterate Boolean
 maps:  results that  are  theoretically obtained  in  that domain  are
 preserved in implementations.
 
 Let us denote by $\llbracket  a ; b \rrbracket$ the following interval

-of integers:  $\{a, a+1,  \hdots, b\}$, where $a~<~b$.  A  {\it system}
+of integers:  $\{a, a+1,  \hdots, b\}$, where $a~<~b$.  
+In that section,  a  system 

 under   consideration    iteratively   modifies   a    collection   of
 $n$~components.   Each component  $i \in  \llbracket 1;  n \rrbracket$
 under   consideration    iteratively   modifies   a    collection   of
 $n$~components.   Each component  $i \in  \llbracket 1;  n \rrbracket$

-takes  its  value  $x_i$  among the  domain  $\Bool=\{0,1\}$.   A~{\it
-  configuration} of the system at discrete time $t$ (also said at {\it
-  iteration} $t$) is the vector
+takes  its  value  $x_i$  among the  domain  $\Bool=\{0,1\}$.   
+A \emph{configuration} of the system at discrete time $t$  is the vector

 %\begin{equation*}   
 $x^{t}=(x_1^{t},\ldots,x_{n}^{t}) \in \Bool^n$.
 %\end{equation*}
 %\begin{equation*}   
 $x^{t}=(x_1^{t},\ldots,x_{n}^{t}) \in \Bool^n$.
 %\end{equation*}


@@ -315,13 +317,39 @@ $N(i,x)=(x_1,\ldots,\overline{x_i},\ldots,x_n)$  is  the configuration
 obtained by switching the $i-$th component of $x$ ($\overline{x_i}$ is
 indeed  the negation  of $x_i$).   Intuitively, $x$  and  $N(i,x)$ are
 neighbors.   The discrete  iterations of  $f$ are  represented  by the
 obtained by switching the $i-$th component of $x$ ($\overline{x_i}$ is
 indeed  the negation  of $x_i$).   Intuitively, $x$  and  $N(i,x)$ are
 neighbors.   The discrete  iterations of  $f$ are  represented  by the

-oriented  {\it graph  of iterations}  $\Gamma(f)$.  In  such  a graph,
+oriented  \emph{graph  of iterations}  $\Gamma(f)$.  In  such  a graph,

 vertices are configurations  of $\Bool^n$ and there is  an arc labeled
 $i$ from $x$ to $N(i,x)$ if and only if  $f_i(x)$ is $N(i,x)$.
 
 vertices are configurations  of $\Bool^n$ and there is  an arc labeled
 $i$ from $x$ to $N(i,x)$ if and only if  $f_i(x)$ is $N(i,x)$.
 

-In the  sequel, the  {\it strategy} $S=(S^{t})^{t  \in \Nats}$  is the
-sequence  defining the  component to  update at  time $t$  and $S^{t}$
-denotes its  $t-$th term.  We  introduce the function $F_{f}$  that is
+In the  sequel, the  \emph{strategy} $S=(S^{t})^{t  \in \Nats}$  is the
+sequence  defining which  component to  update at  time $t$  and $S^{t}$
+denotes its  $t-$th term. 
+This iteration scheme that only modifies one element at each iteration 
+is classically referred as \emph{asynchronous iterations}.
+More precisely, we have here for any $i$, $1\le i \le n$,  
+$$
+\left\{ \begin{array}{l}
+x^{0}  \in \Bool^n \\ 
+x^{t+1}_i = \left\{ 
+\begin{array}{l}
+  f_i(x^t) \textrm{ if $S^t = i$} \\
+  x_i^t \textrm{ otherwise} 
+ \end{array}
+\right.
+\end{array} \right.
+$$
+
+Next section shows the link between asynchronous iterations and 
+Devaney's Chaos.
+
+\subsection{On the link between asynchronous iterations and
+  Devaney's Chaos}
+
+In  this subsection  we recall  the link  we have  established between
+asynchronous iterations and Devaney's  chaos.  The theoretical framework is
+fully described in \cite{guyeux09}.
+
+We  introduce the function $F_{f}$  that is

 defined  for  any given  application  $f:\Bool^{n}  \to \Bool^{n}$  by
 $F_{f}:   \llbracket1;n\rrbracket\times   \mathds{B}^{n}   \rightarrow
 \mathds{B}^{n}$, s.t.
 defined  for  any given  application  $f:\Bool^{n}  \to \Bool^{n}$  by
 $F_{f}:   \llbracket1;n\rrbracket\times   \mathds{B}^{n}   \rightarrow
 \mathds{B}^{n}$, s.t.


@@ -336,7 +364,8 @@ $F_{f}:   \llbracket1;n\rrbracket\times   \mathds{B}^{n}   \rightarrow
     \right. 
 \end{equation}
 
     \right. 
 \end{equation}
 

-\noindent With such a notation, configurations are defined for times 
+\noindent With such a notation, configurations 
+asynchronously obtained are defined for times 

 $t=0,1,2,\ldots$ by:
 \begin{equation}\label{eq:sync}   
 \left\{\begin{array}{l}   
 $t=0,1,2,\ldots$ by:
 \begin{equation}\label{eq:sync}   
 \left\{\begin{array}{l}   


@@ -360,23 +389,20 @@ X^{k+1}& = & G_{f}(X^{k})\\
 \label{eq:Gf}
 \end{equation}
 where  $\sigma$ is the  function that  removes the  first term  of the
 \label{eq:Gf}
 \end{equation}
 where  $\sigma$ is the  function that  removes the  first term  of the

-strategy  ({\it i.e.},~$S^0$).   This definition  means that  only one
-component  of the  system is  updated  at an  iteration: the  $S^t$-th
-element.  But it can be extended by considering subsets for $S^t$.
+strategy  ({\it i.e.},~$S^0$).  
+This definition  allows to links asynchronous iterations with 
+classical iterations of a dynamical system.

 
 

-Let us finally remark that, despite the use of the term {\it chaotic},
-there is {\it  priori} no connection between these  iterations and the
-mathematical theory of chaos presented previously.

 
 

-\subsection{Chaotic Iterations and Devaney's Chaos}
+%means that  only one
+%component  of the  system is  updated  at an  iteration: the  $S^t$-th
+%element. But it can be extended by considering subsets for $S^t$.

 
 

-In  this subsection  we recall  the link  we have  established between
-chaotic iterations and Devaney's  chaos.  The theoretical framework is
-fully described in \cite{guyeux09}.

 
 

-The   {\bf   distance}   $d$    between   two   points   $(S,x)$   and
-$(\check{S},\check{x})\in  \mathcal{X} = \llbracket1;n\rrbracket^\Nats
-\times \Bool^{n}$ is defined by
+To study topological properties of these iterations, we are then left to
+introduce a  {\emph{  distance}}   $d$    between   two   points   $(S,x)$   and
+$(\check{S},\check{x})\in  \mathcal{X} = \llbracket1;n\rrbracket^\Nats. 
+\times \Bool^{n}$. It is defined by

 \begin{equation}
 d((S,x);(\check{S},\check{x}))=d_{e}(x,\check{x})+d_{s}(S,\check{S})
 \enspace ,
 \begin{equation}
 d((S,x);(\check{S},\check{x}))=d_{e}(x,\check{x})+d_{s}(S,\check{S})
 \enspace ,


@@ -392,8 +418,7 @@ d_{s}(S,\check{S})=\frac{9}{2n}\sum_{t=0}^{\infty
 }\frac{|S^{t}-\check{S}^{t}|}{10^{t+1}} \in [0 ; 1] \enspace .
 \end{equation}
 
 }\frac{|S^{t}-\check{S}^{t}|}{10^{t+1}} \in [0 ; 1] \enspace .
 \end{equation}
 

-This    distance    is    defined    to    reflect    the    following
-information. Firstly, the more  two systems have different components,
+Notice that the more  two systems have different components,

 the  larger the  distance  between them is.  Secondly,  two systems  with
 similar components and strategies, which have the same starting terms,
 must  induce only  a small  distance.  The  proposed  distance fulfill
 the  larger the  distance  between them is.  Secondly,  two systems  with
 similar components and strategies, which have the same starting terms,
 must  induce only  a small  distance.  The  proposed  distance fulfill


@@ -403,19 +428,32 @@ measures the difference between the strategies.
 
 The relation  between $\Gamma(f)$ and  $G_f$ is clear: there  exists a
 path from  $x$ to $x'$  in $\Gamma(f)$ if  and only if there  exists a
 
 The relation  between $\Gamma(f)$ and  $G_f$ is clear: there  exists a
 path from  $x$ to $x'$  in $\Gamma(f)$ if  and only if there  exists a

-strategy  $s$ such  that  the  parallel iteration  of  $G_f$ from  the
+strategy  $s$ such  that   iterations  of  $G_f$ from  the

 initial  point $(s,x)$  reaches  the configuration  $x'$.  Using  this
 link, Guyeux~\cite{GuyeuxThese10} has proven that,
 \begin{theorem}%[Characterization of $\mathcal{C}$]
 \label{Th:Caracterisation   des   IC   chaotiques}  
 initial  point $(s,x)$  reaches  the configuration  $x'$.  Using  this
 link, Guyeux~\cite{GuyeuxThese10} has proven that,
 \begin{theorem}%[Characterization of $\mathcal{C}$]
 \label{Th:Caracterisation   des   IC   chaotiques}  

-Let $f:\Bool^n\to\Bool^n$. $G_f$ is chaotic  (according to  Devaney) 
-if and only if  $\Gamma(f)$ is strongly connected.
+Let $f:\Bool^n\to\Bool^n$. Iterations of $G_f$ are chaotic  according 
+to  Devaney if and only if  $\Gamma(f)$ is strongly connected.

 \end{theorem}
 
 \end{theorem}
 

-Checking  if a  graph  is  strongly connected  is  not difficult.  For
-example,  consider the  function $f_1\left(x_1,\dots,x_n\right)=\left(
-\overline{x_1},x_1,x_2,\dots,x_{n-1}\right)$. As $\Gamma(f_1)$ is
-obviously strongly connected, then $G_{f_1}$ is a chaotic map.
+Checking  if a  graph  is  strongly connected  is  not difficult 
+(by the Tarjan's algorithm for instance). 
+Let be given  a strategy $S$ and a function $f$ such that 
+$\Gamma(f)$ is strongly connected.
+In that case, iterations of the function $G_f$ as defined in 
+Eq.~(\ref{eq:Gf}) are chaotic according to Devaney.
+
+
+Let us then define two function $f_0$ and $f_1$ both in 
+$\Bool^n\to\Bool^n$ that are used all along this article. 
+The former is the  vectorial negation, \textit{i.e.},
+$f_{0}(x_{1},\dots,x_{n}) =(\overline{x_{1}},\dots,\overline{x_{n}})$.
+The latter is  $f_1\left(x_1,\dots,x_n\right)=\left(
+\overline{x_1},x_1,x_2,\dots,x_{n-1}\right)$.
+It is not hard to see that  $\Gamma(f_0)$ and $\Gamma(f_1)$ are 
+both strongly connected, then iterations of $G_{f_0}$ and of 
+$G_{f_1}$ are chaotic according to Devaney.

 
 With this  material, we are now  able to build a  first chaotic neural
 network, as defined in the Devaney's formulation.
 
 With this  material, we are now  able to build a  first chaotic neural
 network, as defined in the Devaney's formulation.


@@ -423,14 +461,13 @@ network, as defined in the Devaney's formulation.
 \section{A chaotic neural network in the sense of Devaney}
 \label{S2}
 
 \section{A chaotic neural network in the sense of Devaney}
 \label{S2}
 

-Let     us     firstly     introduce    the     vectorial     negation
-$f_{0}(x_{1},\dots,x_{n}) =(\overline{x_{1}},\dots,\overline{x_{n}})$,
-which is  such that $\Gamma(f_0)$ is  strongly connected.  Considering
-the map  $F_{f_0}:\llbracket 1;  n \rrbracket \times  \mathds{B}^n \to
-\mathds{B}^n$  associated to the  vectorial negation,  we can  build a
-multilayer perceptron  neural network modeling  $F_{f_0}$.  Hence, for
-all inputs  $(s,x) \in \llbracket  1;n\rrbracket \times \mathds{B}^n$,
-the output layer will produce  $F_{f_0}(s,x)$.  It is then possible to
+Firstly, let us build a
+multilayer perceptron  neural network modeling 
+$F_{f_0}:\llbracket 1;  n \rrbracket \times  \mathds{B}^n \to
+\mathds{B}^n$  associated to the  vectorial negation.
+More precisely, for all inputs  
+$(s,x) \in \llbracket  1;n\rrbracket \times \mathds{B}^n$,
+the output layer  produces  $F_{f_0}(s,x)$.  It is then possible to

 link  the output  layer  and the  input  one, in  order  to model  the
 dependence between two successive iterations.  As a result we obtain a
 global  recurrent   neural  network  that  behaves   as  follows  (see
 link  the output  layer  and the  input  one, in  order  to model  the
 dependence between two successive iterations.  As a result we obtain a
 global  recurrent   neural  network  that  behaves   as  follows  (see


@@ -463,15 +500,18 @@ Fig.~\ref{Fig:perceptron}).
 
 The behavior of the neural network is such that when the initial state
 is  $x^0~\in~\mathds{B}^n$ and  a  sequence $(S^t)^{t  \in \Nats}$  is
 
 The behavior of the neural network is such that when the initial state
 is  $x^0~\in~\mathds{B}^n$ and  a  sequence $(S^t)^{t  \in \Nats}$  is

-given  as outside  input, then  the sequence  of  successive published
+given  as outside  input,
+\JFC{en dire davantage sur l'outside world}
+ then  the sequence  of  successive published

 output vectors $\left(x^t\right)^{t \in \mathds{N}^{\ast}}$ is exactly
 the  one produced  by  the chaotic  iterations  formally described  in
 output vectors $\left(x^t\right)^{t \in \mathds{N}^{\ast}}$ is exactly
 the  one produced  by  the chaotic  iterations  formally described  in

-Eq.~(\ref{eq:CIs}).  It  means that  mathematically if we  use similar
+Eq.~(\ref{eq:Gf}).  It  means that  mathematically if we  use similar

 input  vectors   they  both  generate  the   same  successive  outputs
 $\left(x^t\right)^{t \in \mathds{N}^{\ast}}$,  and therefore that they
 are  equivalent  reformulations  of  the iterations  of  $G_{f_0}$  in
 input  vectors   they  both  generate  the   same  successive  outputs
 $\left(x^t\right)^{t \in \mathds{N}^{\ast}}$,  and therefore that they
 are  equivalent  reformulations  of  the iterations  of  $G_{f_0}$  in

-$\mathcal{X}$. Finally, since the  proposed neural network is build to
-model  the  behavior  of  $G_{f_0}$,  which is  chaotic  according  to
+$\mathcal{X}$. Finally, since the  proposed neural network is built to
+model  the  behavior  of  $G_{f_0}$,  whose iterations are
+  chaotic  according  to

 Devaney's definition  of chaos,  we can conclude  that the  network is
 also chaotic in this sense.
 
 Devaney's definition  of chaos,  we can conclude  that the  network is
 also chaotic in this sense.
 


@@ -494,8 +534,8 @@ without any  convincing mathematical proof. We propose  an approach to
 overcome  this  drawback  for  a  particular  category  of  multilayer
 perceptrons defined below, and for the Devaney's formulation of chaos.
 In  spite of  this restriction,  we think  that this  approach  can be
 overcome  this  drawback  for  a  particular  category  of  multilayer
 perceptrons defined below, and for the Devaney's formulation of chaos.
 In  spite of  this restriction,  we think  that this  approach  can be

-extended to  a large variety  of neural networks.  We plan to  study a
-generalization of this approach in a future work.
+extended to  a large variety  of neural networks. 
+

 
 We consider a multilayer perceptron  of the following form: inputs
 are $n$ binary digits and  one integer value, while outputs are  $n$
 
 We consider a multilayer perceptron  of the following form: inputs
 are $n$ binary digits and  one integer value, while outputs are  $n$


@@ -503,7 +543,7 @@ bits.   Moreover, each  binary  output is  connected  with a  feedback
 connection to an input one.
 
 \begin{itemize}
 connection to an input one.
 
 \begin{itemize}

-\item During  initialization, the network if we seed it with $n$~bits denoted
+\item During  initialization, the network is seeded with $n$~bits denoted

   $\left(x^0_1,\dots,x^0_n\right)$  and an  integer  value $S^0$  that
   belongs to $\llbracket1;n\rrbracket$.
 \item     At     iteration~$t$,     the     last     output     vector
   $\left(x^0_1,\dots,x^0_n\right)$  and an  integer  value $S^0$  that
   belongs to $\llbracket1;n\rrbracket$.
 \item     At     iteration~$t$,     the     last     output     vector


@@ -511,6 +551,7 @@ connection to an input one.
   compute the new output one $\left(x^{t+1}_1,\dots,x^{t+1}_n\right)$.
   While  the remaining  input receives  a new  integer value  $S^t \in
   \llbracket1;n\rrbracket$, which is provided by the outside world.
   compute the new output one $\left(x^{t+1}_1,\dots,x^{t+1}_n\right)$.
   While  the remaining  input receives  a new  integer value  $S^t \in
   \llbracket1;n\rrbracket$, which is provided by the outside world.

+\JFC{en dire davantage sur l'outside world}

 \end{itemize}
 
 The topological  behavior of these  particular neural networks  can be
 \end{itemize}
 
 The topological  behavior of these  particular neural networks  can be


@@ -518,23 +559,30 @@ proven to be chaotic through the following process. Firstly, we denote
 by  $F:  \llbracket  1;n  \rrbracket \times  \mathds{B}^n  \rightarrow
 \mathds{B}^n$     the     function     that     maps     the     value
 $\left(s,\left(x_1,\dots,x_n\right)\right)    \in    \llbracket    1;n
 by  $F:  \llbracket  1;n  \rrbracket \times  \mathds{B}^n  \rightarrow
 \mathds{B}^n$     the     function     that     maps     the     value
 $\left(s,\left(x_1,\dots,x_n\right)\right)    \in    \llbracket    1;n

-\rrbracket      \times      \mathds{B}^n$      into     the      value
+\rrbracket      \times      \mathds{B}^n$   
+\JFC{ici, cela devait etre $S^t$ et pas $s$, nn ?}
+   into     the      value

 $\left(y_1,\dots,y_n\right)       \in       \mathds{B}^n$,       where
 $\left(y_1,\dots,y_n\right)$  is the  response of  the  neural network
 after    the    initialization     of    its    input    layer    with
 $\left(y_1,\dots,y_n\right)       \in       \mathds{B}^n$,       where
 $\left(y_1,\dots,y_n\right)$  is the  response of  the  neural network
 after    the    initialization     of    its    input    layer    with

-$\left(s,\left(x_1,\dots, x_n\right)\right)$.  Secondly, we define $f:
+$\left(s,\left(x_1,\dots, x_n\right)\right)$. 
+\JFC{ici, cela devait etre $S^t$ et pas $s$, nn ?}
+Secondly, we define $f:

 \mathds{B}^n       \rightarrow      \mathds{B}^n$       such      that
 $f\left(x_1,x_2,\dots,x_n\right)$ is equal to
 \begin{equation}
 \left(F\left(1,\left(x_1,x_2,\dots,x_n\right)\right),\dots,
   F\left(n,\left(x_1,x_2,\dots,x_n\right)\right)\right) \enspace .
 \end{equation}
 \mathds{B}^n       \rightarrow      \mathds{B}^n$       such      that
 $f\left(x_1,x_2,\dots,x_n\right)$ is equal to
 \begin{equation}
 \left(F\left(1,\left(x_1,x_2,\dots,x_n\right)\right),\dots,
   F\left(n,\left(x_1,x_2,\dots,x_n\right)\right)\right) \enspace .
 \end{equation}

-Then $F=F_f$  and this recurrent  neural network produces  exactly the
-same      output      vectors,      when     feeding      it      with
+Then $F=F_f$. If this recurrent  neural network is seeded with 

 $\left(x_1^0,\dots,x_n^0\right)$    and   $S   \in    \llbracket   1;n
 $\left(x_1^0,\dots,x_n^0\right)$    and   $S   \in    \llbracket   1;n

-\rrbracket^{\mathds{N}}$, than  chaotic iterations $F_f$  with initial
+\rrbracket^{\mathds{N}}$, it produces  exactly the
+same      output      vectors  than the 
+chaotic iterations of $F_f$  with initial

 condition  $\left(S,(x_1^0,\dots,  x_n^0)\right)  \in  \llbracket  1;n
 condition  $\left(S,(x_1^0,\dots,  x_n^0)\right)  \in  \llbracket  1;n

-\rrbracket^{\mathds{N}}  \times \mathds{B}^n$.   In the  rest  of this
+\rrbracket^{\mathds{N}}  \times \mathds{B}^n$.
+Theoretically speaking, such iterations of $F_f$ are thus a formal model of  
+these kind of recurrent neural networks. In the  rest  of this

 paper,  we will  call such  multilayer perceptrons  CI-MLP($f$), which
 stands for ``Chaotic Iterations based MultiLayer Perceptron''.
 
 paper,  we will  call such  multilayer perceptrons  CI-MLP($f$), which
 stands for ``Chaotic Iterations based MultiLayer Perceptron''.
 


@@ -548,6 +596,10 @@ like  topology  to  establish,  for  example,  their  convergence  or,
 contrarily, their unpredictable behavior.   An example of such a study
 is given in the next section.
 
 contrarily, their unpredictable behavior.   An example of such a study
 is given in the next section.
 

+\JFC{Ce qui suit est davantage qu'un exemple.Il faudrait 
+motiver davantage, non?}
+
+

 \section{Topological properties of chaotic neural networks}
 \label{S4}
 
 \section{Topological properties of chaotic neural networks}
 \label{S4}
 


@@ -555,20 +607,23 @@ Let us first recall  two fundamental definitions from the mathematical
 theory of chaos.
 
 \begin{definition} \label{def8}
 theory of chaos.
 
 \begin{definition} \label{def8}

-A  function   $f$  is   said  to  be   {\bf  expansive}   if  $\exists
+A  function   $f$  is   said  to  be   {\emph{  expansive}}   if  $\exists

 \varepsilon>0$, $\forall  x \neq y$,  $\exists n \in  \mathds{N}$ such
 that $d\left(f^n(x),f^n(y)\right) \geq \varepsilon$.
 \end{definition}
 
 \begin{definition} \label{def9}
 \varepsilon>0$, $\forall  x \neq y$,  $\exists n \in  \mathds{N}$ such
 that $d\left(f^n(x),f^n(y)\right) \geq \varepsilon$.
 \end{definition}
 
 \begin{definition} \label{def9}

-A discrete dynamical  system is said to be  {\bf topologically mixing}
+A discrete dynamical  system is said to be  {\emph{ topologically mixing}}

 if  and only  if,  for any  pair  of disjoint  open  sets $U$,$V  \neq
 \emptyset$, we can find some $n_0  \in \mathds{N}$ such that  for any $n$, 
 $n\geq n_0$, we have $f^n(U) \cap V \neq \emptyset$.
 \end{definition}
 if  and only  if,  for any  pair  of disjoint  open  sets $U$,$V  \neq
 \emptyset$, we can find some $n_0  \in \mathds{N}$ such that  for any $n$, 
 $n\geq n_0$, we have $f^n(U) \cap V \neq \emptyset$.
 \end{definition}

+\JFC{Donner un sens à ces definitions}

 
 

-As  proven in Ref.~\cite{gfb10:ip},  chaotic iterations  are expansive
-and  topologically mixing when  $f$ is  the vectorial  negation $f_0$.
+
+It has been proven in Ref.~\cite{gfb10:ip},  that chaotic iterations
+are expansive and  topologically mixing when  $f$ is  the
+vectorial  negation $f_0$.

 Consequently,  these  properties are  inherited  by the  CI-MLP($f_0$)
 recurrent neural network previously presented, which induce a greater
 unpredictability.  Any  difference on the  initial value of  the input
 Consequently,  these  properties are  inherited  by the  CI-MLP($f_0$)
 recurrent neural network previously presented, which induce a greater
 unpredictability.  Any  difference on the  initial value of  the input


@@ -576,12 +631,14 @@ layer is  in particular  magnified up to  be equal to  the expansivity
 constant.
 
 Let us then focus on the consequences for  a neural  network to  be chaotic
 constant.
 
 Let us then focus on the consequences for  a neural  network to  be chaotic

-according  to  Devaney's definition.  First  of  all, the  topological
-transitivity property implies indecomposability.
+according  to  Devaney's definition.  Intuitively, the  topological
+transitivity property implies indecomposability, which is formally defined 
+as follows:
+

 
 \begin{definition} \label{def10}
 
 \begin{definition} \label{def10}

-A   dynamical   system   $\left(   \mathcal{X},  f\right)$   is   {\bf
-indecomposable}  if it  is not  the  union of  two closed  sets $A,  B
+A   dynamical   system   $\left(   \mathcal{X},  f\right)$   is   
+{\emph{not decomposable}}  if it  is not  the  union of  two closed  sets $A,  B

 \subset \mathcal{X}$ such that $f(A) \subset A, f(B) \subset B$.
 \end{definition}
 
 \subset \mathcal{X}$ such that $f(A) \subset A, f(B) \subset B$.
 \end{definition}
 


@@ -591,8 +648,8 @@ strongly connected.  Moreover, under this  hypothesis CI-MLPs($f$) are
 strongly transitive:
 
 \begin{definition} \label{def11}
 strongly transitive:
 
 \begin{definition} \label{def11}

-A  dynamical system  $\left( \mathcal{X},  f\right)$ is  {\bf strongly
-transitive} if $\forall x,y  \in \mathcal{X}$, $\forall r>0$, $\exists
+A  dynamical system  $\left( \mathcal{X},  f\right)$ is  {\emph{ strongly
+transitive}} if $\forall x,y  \in \mathcal{X}$, $\forall r>0$, $\exists

 z  \in  \mathcal{X}$,  $d(z,x)~\leq~r  \Rightarrow  \exists  n  \in
 \mathds{N}^{\ast}$, $f^n(z)=y$.
 \end{definition}
 z  \in  \mathcal{X}$,  $d(z,x)~\leq~r  \Rightarrow  \exists  n  \in
 \mathds{N}^{\ast}$, $f^n(z)=y$.
 \end{definition}


@@ -609,7 +666,8 @@ intrinsically complicated and it cannot be decomposed or simplified.
 Furthermore, those  recurrent neural networks  exhibit the instability
 property:
 \begin{definition}
 Furthermore, those  recurrent neural networks  exhibit the instability
 property:
 \begin{definition}

-A dynamical  system $\left( \mathcal{X}, f\right)$ is  unstable if for
+A dynamical  system $\left( \mathcal{X}, f\right)$ is  \emph{unstable}
+if for

 all  $x  \in  \mathcal{X}$,   the  orbit  $\gamma_x:n  \in  \mathds{N}
 \longmapsto f^n(x)$  is unstable,  that means: $\exists  \varepsilon >
 0$, $\forall  \delta>0$, $\exists y  \in \mathcal{X}$, $\exists  n \in
 all  $x  \in  \mathcal{X}$,   the  orbit  $\gamma_x:n  \in  \mathds{N}
 \longmapsto f^n(x)$  is unstable,  that means: $\exists  \varepsilon >
 0$, $\forall  \delta>0$, $\exists y  \in \mathcal{X}$, $\exists  n \in


@@ -640,7 +698,7 @@ at  least $\varepsilon$  apart in  the metric  $d_n$. Denote  by $H(n,
 \varepsilon)$     the    maximum     cardinality     of    an     $(n,
 \varepsilon)$-separated set,
 \begin{definition}
 \varepsilon)$     the    maximum     cardinality     of    an     $(n,
 \varepsilon)$-separated set,
 \begin{definition}

-The {\it topological entropy} of the  map $f$ is defined by (see e.g.,
+The \emph{topological entropy} of the  map $f$ is defined by (see e.g.,

 Ref.~\cite{Adler65} or Ref.~\cite{Bowen})
 $$h(f)=\lim_{\varepsilon\to      0}      \left(\limsup_{n\to      \infty}
 \frac{1}{n}\log H(n,\varepsilon)\right) \enspace .$$
 Ref.~\cite{Adler65} or Ref.~\cite{Bowen})
 $$h(f)=\lim_{\varepsilon\to      0}      \left(\limsup_{n\to      \infty}
 \frac{1}{n}\log H(n,\varepsilon)\right) \enspace .$$


@@ -652,15 +710,32 @@ $\left( \mathcal{X},d\right)$  is compact and  the topological entropy
 of $(\mathcal{X},G_{f_0})$ is infinite.
 \end{theorem}
 
 of $(\mathcal{X},G_{f_0})$ is infinite.
 \end{theorem}
 

-We have explained how to  construct truly chaotic neural networks, how
-to check whether a  given MLP is chaotic or not, and  how to study its
-topological behavior.   The last thing to  investigate, when comparing
-neural  networks   and  Devaney's  chaos,  is   to  determine  whether
-artificial neural networks  are able to learn or  predict some chaotic
-behaviors, as  it is defined  in the Devaney's formulation  (when they
+\begin{figure}
+  \centering
+  \includegraphics[scale=0.625]{scheme}
+  \caption{Summary of addressed neural networks and chaos problems}
+  \label{Fig:scheme}
+\end{figure}
+
+The Figure~\ref{Fig:scheme} is a summary of addressed neural networks and chaos problems.
+Section~\ref{S2} has explained how to  construct a truly chaotic neural
+networks $A$ for instance.
+Section~\ref{S3} has shown how to check whether a  given MLP
+$A$ or $C$ is chaotic or not in the sens of Devaney.
+%, and  how to study its topological behavior. 
+The last thing to  investigate, when comparing
+neural  networks   and  Devaney's  chaos,  is to  determine  whether
+an artificial neural network $A$  is able to learn or  predict some chaotic
+behaviors of $B$, as  it is defined  in the Devaney's formulation  (when they

 are not specifically constructed for this purpose).  This statement is
 studied in the next section.
 
 are not specifically constructed for this purpose).  This statement is
 studied in the next section.
 

+
+
+
+
+
+

 \section{Suitability of Artificial Neural Networks 
 for Predicting Chaotic Behaviors}
 
 \section{Suitability of Artificial Neural Networks 
 for Predicting Chaotic Behaviors}
 


@@ -719,7 +794,9 @@ such functions into a model amenable to be learned by an ANN.
   
 This  section  presents how  (not)  chaotic  iterations  of $G_f$  are
 translated  into  another  model  more  suited  to  artificial  neural
   
 This  section  presents how  (not)  chaotic  iterations  of $G_f$  are
 translated  into  another  model  more  suited  to  artificial  neural

-networks.  Formally, input and output vectors are pairs~$((S^t)^{t \in
+networks.  
+\JFC{détailler le more suited}
+Formally, input and output vectors are pairs~$((S^t)^{t \in

 \Nats},x)$ and $\left(\sigma((S^t)^{t \in \Nats}),F_{f}(S^0,x)\right)$
 as defined in~Eq.~(\ref{eq:Gf}).
 
 \Nats},x)$ and $\left(\sigma((S^t)^{t \in \Nats}),F_{f}(S^0,x)\right)$
 as defined in~Eq.~(\ref{eq:Gf}).
 


@@ -787,10 +864,10 @@ in   particular    well-known   for   its    universal   approximation
 property. Furthermore,  MLPs have been already  considered for chaotic
 time series prediction.  For example, in~\cite{dalkiran10} the authors
 have shown that a feedforward  MLP with two hidden layers, and trained
 property. Furthermore,  MLPs have been already  considered for chaotic
 time series prediction.  For example, in~\cite{dalkiran10} the authors
 have shown that a feedforward  MLP with two hidden layers, and trained

-with Bayesian  Regulation backpropagation, can  learn successfully the
+with Bayesian  Regulation back-propagation, can  learn successfully the

 dynamics of Chua's circuit.
 
 dynamics of Chua's circuit.
 

-In these experimentations we consider  MLPs having one hidden layer of
+In these experiments  we consider  MLPs having one hidden layer of

 sigmoidal  neurons  and  output   neurons  with  a  linear  activation
 function.     They    are    trained    using    the    Limited-memory
 Broyden-Fletcher-Goldfarb-Shanno quasi-newton algorithm in combination
 sigmoidal  neurons  and  output   neurons  with  a  linear  activation
 function.     They    are    trained    using    the    Limited-memory
 Broyden-Fletcher-Goldfarb-Shanno quasi-newton algorithm in combination


@@ -900,7 +977,7 @@ configuration is always expressed as  a natural number, whereas in the
 first one  the number  of inputs follows  the increase of  the boolean
 vectors coding configurations. In this latter case, the coding gives a
 finer information on configuration evolution.
 first one  the number  of inputs follows  the increase of  the boolean
 vectors coding configurations. In this latter case, the coding gives a
 finer information on configuration evolution.

-
+\JFC{Je n'ai pas compris le paragraphe precedent. Devrait être repris}

 \begin{table}[b]
 \caption{Prediction success rates for configurations expressed with Gray code}
 \label{tab2}
 \begin{table}[b]
 \caption{Prediction success rates for configurations expressed with Gray code}
 \label{tab2}


@@ -1054,6 +1131,21 @@ consequences in biology, physics, and computer science security fields
 will be  stated.  Lastly,  thresholds separating systems  depending on
 the ability to learn their dynamics will be established.
 
 will be  stated.  Lastly,  thresholds separating systems  depending on
 the ability to learn their dynamics will be established.
 

+% \appendix{}
+
+
+
+% \begin{definition} \label{def2}
+% A continuous function $f$  on a topological space $(\mathcal{X},\tau)$
+% is defined  to be  {\emph{topologically transitive}}  if for any  pair of
+% open  sets $U$,  $V  \in  \mathcal{X}$ there  exists  
+% $k \in
+% \mathds{N}^{\ast}$
+%  such  that
+% $f^k(U) \cap V \neq \emptyset$.
+% \end{definition}
+
+

 \bibliography{chaos-paper}% Produces the bibliography via BibTeX.
 
 \end{document}
 \bibliography{chaos-paper}% Produces the bibliography via BibTeX.
 
 \end{document}