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\begin{document}

\title[Recurrent Neural Networks and Chaos]{Recurrent Neural Networks 
and Chaos: Construction, Evaluation, \\
and Prediction Ability}

\author{Jacques M. Bahi}
\author{Jean-Fran\c{c}ois Couchot}
\author{Christophe Guyeux}
\email{christophe.guyeux@univ-fcomte.fr.}
\author{Michel Salomon}
\altaffiliation[Authors in ]{alphabetic order} 
\affiliation{
Computer Science Laboratory (LIFC), University of Franche-Comt\'e, \\
IUT de Belfort-Montb\'eliard, BP 527, \\
90016 Belfort Cedex, France
}

\date{\today}

\newcommand{\CG}[1]{\begin{color}{red}\textit{#1}\end{color}}

\begin{abstract}
%% Text of abstract
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
framework, an equivalence between  chaotic iterations according to the
Devaney's  formulation  of chaos  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
states, 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.
\end{abstract}

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\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 without  any rigorous mathematical proof.   Therefore, in this
work  a theoretical  framework based  on the  Devaney's  definition of
chaos  is introduced.   Starting with  a relationship  between chaotic
iterations  and  Devaney's chaos,  we  firstly  show  how to  build  a
recurrent  neural networks  that is  equivalent to  a chaotic  map and
secondly  a way  to check  whether  an already  available network,  is
chaotic  or not.  We also  study different  topological  properties of
these  truly  chaotic neural  networks.   Finally,  we  show that  the
learning,  with neural  networks  having a  feedforward structure,  of
chaotic behaviors represented by data sets obtained from chaotic maps,
is far more difficult than non chaotic behaviors.
\end{quotation}

%% Main text
\section{Introduction}
\label{S1}

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
memories~\cite{Crook2007267}  and  digital  security tools  like  hash
functions~\cite{Xiao10},                                       digital
watermarking~\cite{1309431,Zhang2005759},           or          cipher
schemes~\cite{Lian20091296}.  In the  former case, the background idea
is to  control chaotic dynamics in  order to store  patterns, with the
key advantage  of offering a  large storage capacity.  For  the latter
case,   the  use   of   chaotic  dynamics   is   motivated  by   their
unpredictability and  random-like behaviors.  Thus,  investigating new
concepts is crucial in this  field, because new threats are constantly
emerging.   As an illustrative  example, the  former standard  in hash
functions,  namely the  SHA-1  algorithm, has  been recently  weakened
after flaws were discovered.

Chaotic neural networks have  been built with different approaches. In
the context of associative  memory, chaotic neurons like the nonlinear
dynamic  state neuron  \cite{Crook2007267}  frequently constitute  the
nodes of the network. These neurons have an inherent chaotic behavior,
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
their universal approximator capacity. 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
chaotic,      is      itself      claimed      to      be      chaotic
\cite{springerlink:10.1007/s00521-010-0432-2}.

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
\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
iterations and  a class of globally  recurrent MLP.
The investigation the  converse problem is the second contribution: 
we indeed study the  ability for
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
been  previously established  that such  dynamical systems  can behave
chaotically, 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
far  more  difficult  for chaotic  ones.   That  is  to say,  we  have
discovered at  least one  family of problems  with a  reasonable size,
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
section is devoted  to the basics of chaotic  iterations and 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
is chaotic  or not. 
Topological properties  of   chaotic  neural  networks  
are discussed in Sect.~\ref{S4}.   The
Section~\ref{section:translation}   shows   how   to  translate   such
iterations  into  an Artificial  Neural  Network  (ANN),  in order  to
evaluate the  capability for this  latter to learn  chaotic behaviors.
This  ability  is  studied in  Sect.~\ref{section:experiments},  where
various ANNs try to learn two  sets of data: the first one is obtained
by chaotic iterations while the  second one results from a non-chaotic
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.

\section{Link between Chaotic Iterations and Devaney's Chaos}

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
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).

\subsection{Devaney's chaotic dynamical systems}

Various domains such as  physics, biology, or economy, contain systems
that exhibit a chaotic behavior,  a well-known example is the weather.
These  systems   are  in   particular  highly  sensitive   to  initial
conditions, a concept usually presented as the butterfly effect: small
variations in the initial conditions possibly lead to widely different
behaviors.  Theoretically speaking, a  system is sensitive if for each
point  $x$ in  the  iteration space,  one  can find  a  point in  each
neighborhood of $x$ having a significantly different future evolution.
Conversely, a  system seeded with  the same initial  conditions always
has  the same  evolution.   In  other words,  chaotic  systems have  a
deterministic  behavior  defined through  a  physical or  mathematical
model  and a  high  sensitivity to  the  initial conditions.   Besides
mathematically this  kind of unpredictability  is also referred  to as
deterministic chaos.  For example, many weather forecast models exist,
but they give only suitable predictions for about a week, because they
are initialized with conditions  that reflect only a partial knowledge
of the current weather.  Even  the  differences are  initially small,
they are  amplified in the course  of time, and thus  make difficult a
long-term prediction.  In fact,  in a chaotic system, an approximation
of  the current  state is  a  quite useless  indicator for  predicting
future states.

From  mathematical  point  of   view,  deterministic  chaos  has  been
thoroughly studied  these last decades, with  different research works
that  have   provide  various  definitions  of   chaos.   Among  these
definitions,   the  one  given   by  Devaney~\cite{Devaney}   is  
well-established.    This   definition   consists  of   three   conditions:
topological  transitivity, density of  periodic points,  and sensitive
point dependence on initial conditions.

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

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}

Finally,

\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}.

\subsection{Chaotic Iterations}

This section  presents some basics on  topological chaotic iterations.
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
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}
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
%\begin{equation*}   
$x^{t}=(x_1^{t},\ldots,x_{n}^{t}) \in \Bool^n$.
%\end{equation*}
The dynamics of the system is  described according to a  function $f :
\Bool^n \rightarrow \Bool^n$ such that
%\begin{equation*} 
$f(x)=(f_1(x),\ldots,f_n(x))$.
%\end{equation*}
% Notice that $f^k$ denotes the 
% $k-$th composition $f\circ \ldots \circ f$ of the function $f$.

Let    be   given    a    configuration   $x$.    In   what    follows
$N(i,x)=(x_1,\ldots,\overline{x_i},\ldots,x_n)$  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
oriented  {\it 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 $N(i,x)$.

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
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.
\begin{equation}
\label{eq:CIs}
  F_{f}(s,x)_j  =  
    \left\{
    \begin{array}{l}
    f_j(x) \textrm{ if } j= s \enspace , \\ 
    x_{j} \textrm{ otherwise} \enspace .
    \end{array}
    \right. 
\end{equation}

\noindent With such a notation, configurations are defined for times 
$t=0,1,2,\ldots$ by:
\begin{equation}\label{eq:sync}   
\left\{\begin{array}{l}   
  x^{0}\in \mathds{B}^{n} \textrm{ and}\\
  x^{t+1}=F_{f}(S^t,x^{t}) \enspace .
\end{array}\right.
\end{equation}

\noindent  Finally, iterations defined  in Eq.~(\ref{eq:sync})  can be
described by the following system:
\begin{equation} 
\left\{
\begin{array}{lll} 
X^{0} & = &  ((S^t)^{t \in \Nats},x^0) \in 
\llbracket1;n\rrbracket^\Nats \times \Bool^{n}\\ 
X^{k+1}& = & G_{f}(X^{k})\\
\multicolumn{3}{c}{\textrm{where } G_{f}\left(((S^t)^{t \in \Nats},x)\right)
= \left(\sigma((S^t)^{t \in \Nats}),F_{f}(S^0,x)\right) \enspace ,}
\end{array} 
\right.
\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 $S^t$.

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}

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
\begin{equation}
d((S,x);(\check{S},\check{x}))=d_{e}(x,\check{x})+d_{s}(S,\check{S})
\enspace ,
\end{equation}
where
\begin{equation}
d_{e}(x,\check{x})=\sum_{j=1}^{n}\Delta
(x_{j},\check{x}_{j}) \in \llbracket  0 ; n \rrbracket 
\end{equation}
\noindent and   
\begin{equation}
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}

This    distance    is    defined    to    reflect    the    following
information. Firstly, 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
these  requirements: on  the one  hand  its floor  value reflects  the
difference between  the cells, on  the other hand its  fractional part
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
strategy  $s$ such  that  the  parallel iteration  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}  
Let $f:\Bool^n\to\Bool^n$. $G_f$ is chaotic  (according to  Devaney) 
if and only if  $\Gamma(f)$ is strongly connected.
\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.

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

\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
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
Fig.~\ref{Fig:perceptron}).

\begin{itemize}
\item   The   network   is   initialized   with   the   input   vector
  $\left(S^0,x^0\right)    \in    \llbracket   1;n\rrbracket    \times
  \mathds{B}^n$      and      computes      the     output      vector
  $x^1=F_{f_0}\left(S^0,x^0\right)$. This last  vector is published as
  an output one of the chaotic  neural network and is sent back to the
  input layer through the feedback links.
\item When  the network  is activated at  the $t^{th}$  iteration, the
  state of the system $x^t  \in \mathds{B}^n$ received from the output
  layer and  the initial  term of the  sequence $(S^t)^{t  \in \Nats}$
  ($S^0  \in \llbracket 1;n\rrbracket$)  are used  to compute  the new
  output.   This new  output, which  represents the  new state  of the
  dynamical system, satisfies:
  \begin{equation}
    x^{t+1}=F_{f_0}(S^0, x^t) \in \mathds{B}^n \enspace .
  \end{equation}
\end{itemize}

\begin{figure}
  \centering
  \includegraphics[scale=0.625]{perceptron}
  \caption{A perceptron equivalent to chaotic iterations}
  \label{Fig:perceptron}
\end{figure}

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

The previous construction scheme  is not restricted to function $f_0$.
It can  be extended to any function  $f$ such that $G_f$  is a chaotic
map by training the network to model $F_{f}:\llbracket 1; n \rrbracket
\times      \mathds{B}^n      \to      \mathds{B}^n$.      Due      to
Theorem~\ref{Th:Caracterisation  des  IC  chaotiques},  we  can  find
alternative functions  $f$ for $f_0$  through a simple check  of their
graph of  iterations $\Gamma(f)$.  For  example, we can  build another
chaotic neural network by using $f_1$ instead of $f_0$.

\section{Checking whether a neural network is chaotic or not}
\label{S3}

We focus now on the case  where a neural network is already available,
and for which we  want to know if it is chaotic. Typically, in
many research papers neural network  are usually claimed to be chaotic
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
extended to  a large variety  of neural networks.  We plan to  study a
generalization of this approach in a future work.

We consider a multilayer perceptron  of the following form: inputs
are $n$ binary digits and  one integer value, while outputs are  $n$
bits.   Moreover, each  binary  output is  connected  with a  feedback
connection to an input one.

\begin{itemize}
\item During  initialization, the network if we seed it 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
  $\left(x^t_1,\dots,x^t_n\right)$   defines  the  $n$~bits   used  to
  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.
\end{itemize}

The topological  behavior of these  particular neural networks  can be
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
$\left(s,\left(x_1,\dots,x_n\right)\right)    \in    \llbracket    1;n
\rrbracket      \times      \mathds{B}^n$      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
$\left(s,\left(x_1,\dots, x_n\right)\right)$.  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}
Then $F=F_f$  and this recurrent  neural network produces  exactly the
same      output      vectors,      when     feeding      it      with
$\left(x_1^0,\dots,x_n^0\right)$    and   $S   \in    \llbracket   1;n
\rrbracket^{\mathds{N}}$, than  chaotic iterations $F_f$  with initial
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
paper,  we will  call such  multilayer perceptrons  CI-MLP($f$), which
stands for ``Chaotic Iterations based MultiLayer Perceptron''.

Checking  if CI-MLP($f$)  behaves chaotically  according  to Devaney's
definition  of  chaos  is  simple:  we  need just  to  verify  if  the
associated graph  of iterations  $\Gamma(f)$ is strongly  connected or
not. As  an incidental consequence,  we finally obtain  an equivalence
between  chaotic  iterations   and  CI-MLP($f$).   Therefore,  we  can
obviously  study such multilayer  perceptrons with  mathematical tools
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.

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

Let us first recall  two fundamental definitions from the mathematical
theory of chaos.

\begin{definition} \label{def8}
A  function   $f$  is   said  to  be   {\bf  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}
A discrete dynamical  system is said to be  {\bf 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}

As  proven in Ref.~\cite{gfb10:ip},  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
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
according  to  Devaney's definition.  First  of  all, the  topological
transitivity property implies indecomposability.

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

\noindent Hence, reducing the set of outputs generated by CI-MLP($f$),
in order to  simplify its complexity, is impossible  if $\Gamma(f)$ is
strongly connected.  Moreover, under this  hypothesis CI-MLPs($f$) are
strongly transitive:

\begin{definition} \label{def11}
A  dynamical system  $\left( \mathcal{X},  f\right)$ is  {\bf strongly
transitive} if $\forall x,y  \in \mathcal{X}$, $\forall r>0$, $\exists
z  \in  \mathcal{X}$,  $d(z,x)~\leq~r  \Rightarrow  \exists  n  \in
\mathds{N}^{\ast}$, $f^n(z)=y$.
\end{definition}
According to this definition, for all  pairs of points $(x, y)$ in the
phase space, a point $z$ can  be found in the neighborhood of $x$ such
that one of its iterates $f^n(z)$ is $y$. Indeed, this result has been
established  during  the  proof   of  the  transitivity  presented  in
Ref.~\cite{guyeux09}.  Among  other  things, the  strong  transitivity
leads  to the fact  that without  the knowledge  of the  initial input
layer, all outputs are possible.  Additionally, no point of the output
space  can   be  discarded  when  studying  CI-MLPs:   this  space  is
intrinsically complicated and it cannot be decomposed or simplified.

Furthermore, those  recurrent neural networks  exhibit the instability
property:
\begin{definition}
A dynamical  system $\left( \mathcal{X}, f\right)$ is  unstable if for
all  $x  \in  \mathcal{X}$,   the  orbit  $\gamma_x:n  \in  \mathds{N}
\longmapsto f^n(x)$  is unstable,  that means: $\exists  \varepsilon >
0$, $\forall  \delta>0$, $\exists y  \in \mathcal{X}$, $\exists  n \in
\mathds{N}$,        such        that        $d(x,y)<\delta$        and
$d\left(\gamma_x(n),\gamma_y(n)\right) \geq \varepsilon$.
\end{definition}
This property, which  is implied by the sensitive  point dependence on
initial conditions, leads to the fact that in all neighborhoods of any
point $x$, there are points that  can be apart by $\varepsilon$ in the
future through iterations of the  CI-MLP($f$). Thus, we can claim that
the behavior of  these MLPs is unstable when  $\Gamma (f)$ is strongly
connected.

Let  us  now consider  a  compact  metric space  $(M,  d)$  and $f:  M
\rightarrow M$  a continuous map. For  each natural number  $n$, a new
metric $d_n$ is defined on $M$ by
$$d_n(x,y)=\max\{d(f^i(x),f^i(y)): 0\leq i<n\} \enspace .$$

Given any $\varepsilon > 0$ and $n \geqslant 1$, two points of $M$ are
$\varepsilon$-close  with respect to  this metric  if their  first $n$
iterates are $\varepsilon$-close.

This metric  allows one to distinguish  in a neighborhood  of an orbit
the points  that move away from  each other during  the iteration from
the points  that travel together.  A subset  $E$ of $M$ is  said to be
$(n, \varepsilon)$-separated if each pair of distinct points of $E$ is
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}
The {\it topological entropy} of the  map $f$ is defined by (see e.g.,
Ref.~\cite{Adler65} or Ref.~\cite{Bowen})
$$h(f)=\lim_{\varepsilon\to      0}      \left(\limsup_{n\to      \infty}
\frac{1}{n}\log H(n,\varepsilon)\right) \enspace .$$
\end{definition}

Then we have the following result \cite{GuyeuxThese10},
\begin{theorem}
$\left( \mathcal{X},d\right)$  is compact and  the topological entropy
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
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}

In  the context  of computer  science  different topic  areas have  an
interest       in       chaos,       as       for       steganographic
techniques~\cite{1309431,Zhang2005759}.    Steganography  consists  in
embedding a  secret message within  an ordinary one, while  the secret
extraction takes place once  at destination.  The reverse ({\it i.e.},
automatically detecting the presence  of hidden messages inside media)
is  called   steganalysis.   Among  the   deployed  strategies  inside
detectors,          there         are          support         vectors
machines~\cite{Qiao:2009:SM:1704555.1704664},                    neural
networks~\cite{10.1109/ICME.2003.1221665,10.1109/CIMSiM.2010.36},  and
Markov   chains~\cite{Sullivan06steganalysisfor}.    Most   of   these
detectors  give quite  good results  and are  rather  competitive when
facing steganographic  tools.  However, to  the best of  our knowledge
none of the considered information hiding schemes fulfills the Devaney
definition  of chaos~\cite{Devaney}.  Indeed,  one can  wonder whether
detectors  continue to  give good  results when  facing  truly chaotic
schemes.  More  generally, there remains the open  problem of deciding
whether artificial intelligence is suitable for predicting topological
chaotic behaviors.

\subsection{Representing Chaotic Iterations for Neural Networks} 
\label{section:translation}

The  problem  of  deciding  whether  classical  feedforward  ANNs  are
suitable  to approximate  topological chaotic  iterations may  then be
reduced  to evaluate  ANNs on  iterations of  functions  with Strongly
Connected  Component  (SCC)~graph  of  iterations.   To  compare  with
non-chaotic  iterations,  the experiments  detailed  in the  following
sections are  carried out  using both kinds  of function  (chaotic and
non-chaotic). Let us emphasize on  the difference between this kind of
neural   networks  and  the   Chaotic  Iterations   based  MultiLayer
Perceptron.

We are  then left to compute  two disjoint function  sets that contain
either functions  with topological chaos properties  or not, depending
on  the strong  connectivity of  their iterations graph.  This  can be
achieved for  instance by removing a  set of edges  from the iteration
graph $\Gamma(f_0)$ of the vectorial negation function~$f_0$.  One can
deduce whether  a function verifies the topological  chaos property or
not  by checking  the strong  connectivity of  the resulting  graph of
iterations.

For instance let us consider  the functions $f$ and $g$ from $\Bool^4$
to $\Bool^4$ respectively defined by the following lists:
$$[0,  0,  2,   3,  13,  13,  6,   3,  8,  9,  10,  11,   8,  13,  14,
  15]$$ $$\mbox{and } [11, 14, 13, 14, 11, 10, 1, 8, 7, 6, 5, 4, 3, 2,
  1, 0]  \enspace.$$ In  other words,  the image of  $0011$ by  $g$ is
$1110$: it  is obtained as the  binary value of the  fourth element in
the  second  list  (namely~14).   It   is  not  hard  to  verify  that
$\Gamma(f)$ is  not SCC  (\textit{e.g.}, $f(1111)$ is  $1111$) whereas
$\Gamma(g)$  is.  Next section  shows how  to translate  iterations of
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
networks.  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}).

Firstly, let us focus on how to memorize configurations.  Two distinct
translations are  proposed.  In the first  case, we take  one input in
$\Bool$  per  component;  in   the  second  case,  configurations  are
memorized  as   natural  numbers.    A  coarse  attempt   to  memorize
configuration  as  natural  number  could  consist  in  labeling  each
configuration  with  its  translation  into  decimal  numeral  system.
However,  such a  representation induces  too many  changes  between a
configuration  labeled by  a  power  of two  and  its direct  previous
configuration: for instance, 16~(10000) and 15~(01111) are closed in a
decimal ordering, but  their Hamming distance is 5.   This is why Gray
codes~\cite{Gray47} have been preferred.

Let us secondly detail how to deal  with strategies.   Obviously,  it is  not
possible to  translate in a finite  way an infinite  strategy, even if
both $(S^t)^{t \in \Nats}$ and $\sigma((S^t)^{t \in \Nats})$ belong to
$\{1,\ldots,n\}^{\Nats}$.  Input strategies are then reduced to have a
length  of size  $l  \in  \llbracket 2,k\rrbracket$,  where  $k$ is  a
parameter of the evaluation. Notice  that $l$ is greater than or equal
to $2$ since  we do not want the shift  $\sigma$~function to return an
empty strategy.  Strategies are memorized as natural numbers expressed
in base  $n+1$.  At  each iteration, either  none or one  component is
modified  (among the  $n$ components)  leading to  a radix  with $n+1$
entries.  Finally,  we give an  other input, namely $m  \in \llbracket
1,l-1\rrbracket$, which  is the  number of successive  iterations that
are applied starting  from $x$.  Outputs are translated  with the same
rules.

To address  the complexity  issue of the  problem, let us  compute the
size of the data set an ANN has to deal with.  Each input vector of an
input-output pair  is composed of a configuration~$x$,  an excerpt $S$
of the strategy to iterate  of size $l \in \llbracket 2, k\rrbracket$,
and a  number $m \in  \llbracket 1, l-1\rrbracket$ of  iterations that
are executed.

Firstly, there are $2^n$  configurations $x$, with $n^l$ strategies of
size $l$ for  each of them. Secondly, for  a given configuration there
are $\omega = 1 \times n^2 +  2 \times n^3 + \ldots+ (k-1) \times n^k$
ways  of writing  the pair  $(m,S)$. Furthermore,  it is  not  hard to
establish that
\begin{equation}
\displaystyle{(n-1) \times \omega = (k-1)\times n^{k+1} - \sum_{i=2}^k n^i} \nonumber
\end{equation}
then
\begin{equation}
\omega =
\dfrac{(k-1)\times n^{k+1}}{n-1} - \dfrac{n^{k+1}-n^2}{(n-1)^2} \enspace . \nonumber
\end{equation}
\noindent And then, finally, the number of  input-output pairs for our 
ANNs is 
$$
2^n \times \left(\dfrac{(k-1)\times n^{k+1}}{n-1} - \dfrac{n^{k+1}-n^2}{(n-1)^2}\right) \enspace .
$$
For  instance, for $4$  binary components  and a  strategy of  at most
$3$~terms we obtain 2304~input-output pairs.

\subsection{Experiments}
\label{section:experiments}

To study  if chaotic iterations can  be predicted, we  choose to train
the MultiLayer Perceptron.  As stated  before, this kind of network is
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
with Bayesian  Regulation backpropagation, can  learn successfully the
dynamics of Chua's circuit.

In these experimentations 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
with the Wolfe linear search.  The training process is performed until
a maximum number of epochs  is reached.  To prevent overfitting and to
estimate the  generalization performance we use  holdout validation by
splitting the  data set into  learning, validation, and  test subsets.
These subsets  are obtained through  random selection such  that their
respective size represents 65\%, 10\%, and 25\% of the whole data set.

Several  neural  networks  are  trained  for  both  iterations  coding
schemes.   In  both  cases   iterations  have  the  following  layout:
configurations of  four components and  strategies with at  most three
terms. Thus, for  the first coding scheme a data  set pair is composed
of 6~inputs and 5~outputs, while for the second one it is respectively
3~inputs and 2~outputs. As noticed at the end of the previous section,
this  leads to  data sets  that  consist of  2304~pairs. The  networks
differ  in the  size of  the hidden  layer and  the maximum  number of
training epochs.  We remember that  to evaluate the ability  of neural
networks to  predict a  chaotic behavior for  each coding  scheme, the
trainings of two data sets, one of them describing chaotic iterations,
are compared.

Thereafter we give,  for the different learning setups  and data sets,
the  mean prediction  success rate  obtained for  each  output.  These
values  are  computed  considering  10~trainings with  random  subsets
construction,  weights  and  biases initialization.   Firstly,  neural
networks having 10  and 25~hidden neurons are trained,  with a maximum
number  of  epochs that  takes  its  value  in $\{125,250,500\}$  (see
Tables~\ref{tab1}  and \ref{tab2}).   Secondly, we  refine  the second
coding scheme by splitting the  output vector such that each output is
learned by a specific  neural network (Table~\ref{tab3}). In this last
case, we increase  the size of the hidden layer  up to 40~neurons, and
we consider larger number of epochs.

\begin{table}[htbp!]
\caption{Prediction success rates for configurations expressed as boolean vectors.}
\label{tab1}
\centering {\small
\begin{tabular}{|c|c||c|c|c|}
\hline 
\multicolumn{5}{|c|}{Networks topology: 6~inputs, 5~outputs and one hidden layer} \\
\hline
\hline
\multicolumn{2}{|c||}{Hidden neurons} & \multicolumn{3}{c|}{10 neurons} \\
\cline{3-5} 
\multicolumn{2}{|c||}{Epochs} & 125 & 250 & 500 \\ 
\hline
\multirow{6}{*}{\rotatebox{90}{Chaotic}}&Output~(1) & 90.92\% & 91.75\% & 91.82\% \\ 
& Output~(2) & 69.32\% & 78.46\% & 82.15\% \\
& Output~(3) & 68.47\% & 78.49\% & 82.22\% \\
& Output~(4) & 91.53\% & 92.37\% & 93.4\% \\
& Config. & 36.10\% & 51.35\% & 56.85\% \\
& Strategy~(5) & 1.91\% & 3.38\% & 2.43\% \\
\hline
\multirow{6}{*}{\rotatebox{90}{Non-chaotic}}&Output~(1) & 97.64\% & 98.10\% & 98.20\% \\
& Output~(2) & 95.15\% & 95.39\% & 95.46\% \\
& Output~(3) & 100\% & 100\% & 100\% \\
& Output~(4) & 97.47\% & 97.90\% & 97.99\% \\
& Config. & 90.52\% & 91.59\% & 91.73\% \\
& Strategy~(5) & 3.41\% & 3.40\% & 3.47\% \\
\hline
\hline
\multicolumn{2}{|c||}{Hidden neurons} & \multicolumn{3}{c|}{25 neurons} \\ %& \multicolumn{3}{|c|}{40 neurons} \\
\cline{3-5} 
\multicolumn{2}{|c||}{Epochs} & 125 & 250 & 500 \\ %& 125 & 250 & 500 \\ 
\hline
\multirow{6}{*}{\rotatebox{90}{Chaotic}}&Output~(1) & 91.65\% & 92.69\% & 93.93\% \\ %& 91.94\% & 92.89\% & 94.00\% \\
& Output~(2) & 72.06\% & 88.46\% & 90.5\% \\ %& 74.97\% & 89.83\% & 91.14\% \\
& Output~(3) & 79.19\% & 89.83\% & 91.59\% \\ %& 76.69\% & 89.58\% & 91.84\% \\
& Output~(4) & 91.61\% & 92.34\% & 93.47\% \\% & 82.77\% & 92.93\% & 93.48\% \\
& Config. & 48.82\% & 67.80\% & 70.97\% \\%& 49.46\% & 68.94\% & 71.11\% \\
& Strategy~(5) & 2.62\% & 3.43\% & 3.78\% \\% & 3.10\% & 3.10\% & 3.03\% \\
\hline
\multirow{6}{*}{\rotatebox{90}{Non-chaotic}}&Output~(1) & 97.87\% & 97.99\% & 98.03\% \\ %& 98.16\% \\
& Output~(2) & 95.46\% & 95.84\% & 96.75\% \\ % & 97.4\% \\
& Output~(3) & 100\% & 100\% & 100\% \\%& 100\% \\
& Output~(4) & 97.77\% & 97.82\% & 98.06\% \\%& 98.31\% \\
& Config. & 91.36\% & 91.99\% & 93.03\% \\%& 93.98\% \\
& Strategy~(5) & 3.37\% & 3.44\% & 3.29\% \\%& 3.23\% \\
\hline
\end{tabular}
}
\end{table}

Table~\ref{tab1}  presents the  rates  obtained for  the first  coding
scheme.   For  the chaotic  data,  it can  be  seen  that as  expected
configuration  prediction becomes  better  when the  number of  hidden
neurons and maximum  epochs increases: an improvement by  a factor two
is observed (from 36.10\% for 10~neurons and 125~epochs to 70.97\% for
25~neurons  and  500~epochs). We  also  notice  that  the learning  of
outputs~(2)   and~(3)  is   more  difficult.    Conversely,   for  the
non-chaotic  case the  simplest training  setup is  enough  to predict
configurations.   For  all  network  topologies and  all  outputs  the
obtained  results  for the  non-chaotic  case  outperform the  chaotic
ones. Finally,  the rates for  the strategies show that  the different
networks are unable to learn them.

For the second coding  scheme (\textit{i.e.}, with Gray Codes)
Table~\ref{tab2} shows that any network
learns about  five times more non-chaotic  configurations than chaotic
ones. As in the previous scheme, the strategies cannot be predicted.

Let us now compare the  two coding schemes. Firstly, the second scheme
disturbs  the   learning  process.   In   fact  in  this   scheme  the
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.

\begin{table}[b]
\caption{Prediction success rates for configurations expressed with Gray code}
\label{tab2}
\centering
\begin{tabular}{|c|c||c|c|c|}
\hline 
\multicolumn{5}{|c|}{Networks topology: 3~inputs, 2~outputs and one hidden layer} \\
\hline
\hline
& Hidden neurons & \multicolumn{3}{c|}{10 neurons} \\
\cline{2-5}
& Epochs & 125 & 250 & 500 \\ %& 1000 
\hline
\multirow{2}{*}{Chaotic}& Config.~(1) & 13.29\% & 13.55\% & 13.08\% \\ %& 12.5\%
& Strategy~(2) & 0.50\% & 0.52\% & 1.32\% \\ %& 1.42\%
\hline
\multirow{2}{*}{Non-Chaotic}&Config.~(1) & 77.12\% & 74.00\% & 72.60\% \\ %& 75.81\% 
& Strategy~(2) & 0.42\% & 0.80\% & 1.16\% \\ %& 1.42\% 
\hline
\hline
& Hidden neurons & \multicolumn{3}{c|}{25 neurons} \\
\cline{2-5}
& Epochs & 125 & 250 & 500 \\ %& 1000 
\hline
\multirow{2}{*}{Chaotic}& Config.~(1) & 12.27\% & 13.15\% & 13.05\% \\ %& 15.44\%
& Strategy~(2) & 0.71\% & 0.66\% & 0.88\% \\ %& 1.73\%
\hline
\multirow{2}{*}{Non-Chaotic}&Config.~(1) & 73.60\% & 74.70\% & 75.89\% \\ %& 68.32\%
& Strategy~(2) & 0.64\% & 0.97\% & 1.23\% \\ %& 1.80\%
\hline
\end{tabular}
\end{table}

Unfortunately, in  practical applications the number  of components is
usually  unknown.   Hence, the  first  coding  scheme  cannot be  used
systematically.   Therefore, we  provide  a refinement  of the  second
scheme: each  output is learned  by a different  ANN. Table~\ref{tab3}
presents the  results for  this approach.  In  any case,  whatever the
network  topologies,  the  maximum   epoch  number  and  the  kind  of
iterations,  the  configuration  success  rate is  slightly  improved.
Moreover, the strategies predictions  rates reach almost 12\%, whereas
in  Table~\ref{tab2}  they  never   exceed  1.5\%.   Despite  of  this
improvement, a long term prediction of chaotic iterations still 
appear to be
an open issue.

\begin{table}
\caption{Prediction success rates for split outputs.}
\label{tab3}
\centering
\begin{tabular}{|c||c|c|c|}
\hline 
\multicolumn{4}{|c|}{Networks topology: 3~inputs, 1~output and one hidden layer} \\
\hline
\hline
Epochs & 125 & 250 & 500 \\ 
\hline
\hline
Chaotic & \multicolumn{3}{c|}{Output = Configuration} \\
\hline
10~neurons & 12.39\% & 14.06\% & 14.32\% \\
25~neurons & 13.00\% & 14.28\% & 14.58\% \\
40~neurons & 11.58\% & 13.47\% & 14.23\% \\
\hline
\hline
Non chaotic & \multicolumn{3}{c|}{Output = Configuration} \\
\cline{2-4}
%Epochs & 125 & 250 & 500 \\ 
\hline
10~neurons & 76.01\% & 74.04\% & 78.16\% \\
25~neurons & 76.60\% & 72.13\% & 75.96\% \\
40~neurons & 76.34\% & 75.63\% & 77.50\% \\
\hline
\hline
Chaotic/non chaotic & \multicolumn{3}{c|}{Output = Strategy} \\
\cline{2-4}
%Epochs & 125 & 250 & 500 \\ 
\hline
10~neurons & 0.76\% & 0.97\% & 1.21\% \\
25~neurons & 1.09\% & 0.73\% & 1.79\% \\
40~neurons & 0.90\% & 1.02\% & 2.15\% \\
\hline
\multicolumn{4}{c}{} \\
\hline
Epochs & 1000 & 2500 & 5000 \\ 
\hline
\hline
Chaotic & \multicolumn{3}{c|}{Output = Configuration} \\
\hline
10~neurons & 14.51\% & 15.22\% & 15.22\% \\
25~neurons & 16.95\% & 17.57\% & 18.46\% \\
40~neurons & 17.73\% & 20.75\% & 22.62\% \\
\hline
\hline
Non chaotic & \multicolumn{3}{c|}{Output = Configuration} \\
\cline{2-4}
%Epochs & 1000 & 2500 & 5000 \\ 
\hline
10~neurons & 78.98\% & 80.02\% & 79.97\% \\
25~neurons & 79.19\% & 81.59\% & 81.53\% \\
40~neurons & 79.64\% & 81.37\% & 81.37\% \\
\hline
\hline
Chaotic/non chaotic & \multicolumn{3}{c|}{Output = Strategy} \\
\cline{2-4}
%Epochs & 1000 & 2500 & 5000 \\ 
\hline
10~neurons & 3.47\% & 9.98\% & 11.66\% \\
25~neurons & 3.92\% & 8.63\% & 10.09\% \\
40~neurons & 3.29\% & 7.19\% & 7.18\% \\
\hline
\end{tabular}
\end{table}

\section{Conclusion}

In  this paper,  we have  established an  equivalence  between chaotic
iterations,  according to  the Devaney's  definition of  chaos,  and a
class  of  multilayer perceptron  neural  networks.  Firstly, we  have
described how to build a neural network that can be trained to learn a
given chaotic map function.  Then,  we found a condition that allow to
check whether the iterations induced by a function are chaotic or not,
and thus  if a chaotic map  is obtained. Thanks to  this condition our
approach is not limited to a particular function. In the dual case, we
show  that  checking  if  a  neural network  is  chaotic  consists  in
verifying  a property  on an  associated  graph, called  the graph  of
iterations.   These results  are valid  for recurrent  neural networks
with a  particular architecture.  However,  we believe that  a similar
work can be done for  other neural network architectures.  Finally, we
have  discovered at  least one  family of  problems with  a reasonable
size, such  that artificial neural  networks should not be  applied in
the  presence  of chaos,  due  to  their  inability to  learn  chaotic
behaviors in this  context.  Such a consideration is  not reduced to a
theoretical detail:  this family of discrete  iterations is concretely
implemented  in a  new steganographic  method  \cite{guyeux10ter}.  As
steganographic   detectors  embed  tools   like  neural   networks  to
distinguish between  original and stego contents, our  studies tend to
prove that such  detectors might be unable to  tackle with chaos-based
information hiding schemes.  Furthermore, iterations such that not all
of  the  components  are updated  at  each  step  are very  common  in
biological  and  physics mechanisms.   Therefore,  one can  reasonably
wonder whether neural networks should be applied in these contexts.

In  future  work we  intend  to  enlarge  the comparison  between  the
learning   of  truly   chaotic  and   non-chaotic   behaviors.   Other
computational intelligence tools such  as support vector machines will
be investigated  too, to  discover which tools  are the  most relevant
when facing a truly chaotic phenomenon.  A comparison between learning
rate  success  and  prediction  quality will  be  realized.   Concrete
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.

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

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