From e041c0c5dd32ff38f533543cc17b6b2e8ce090ce Mon Sep 17 00:00:00 2001 From: =?utf8?q?Jean-Fran=C3=A7ois=20Couchot?= Date: Thu, 8 Sep 2011 15:57:55 +0200 Subject: [PATCH 1/1] init --- main.tex | 1054 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1054 insertions(+) diff --git a/main.tex b/main.tex index e69de29..aa920cb 100644 --- a/main.tex +++ b/main.tex @@ -0,0 +1,1054 @@ +% ****** Start of file chaos-paper.tex ****** +% +\documentclass[% +aip, +cha,% long, +numerical bibliography, %(default) +%jcp,% short, numerical bibliography, +%jmp, +amsmath,amssymb, +preprint,% +%reprint,% +%author-year,% +%author-numerical,% +]{revtex4-1} + +\usepackage{graphicx}% Include figure files +\usepackage{dcolumn}% Align table columns on decimal point +\usepackage{bm}% bold math +\usepackage{color} +\usepackage{ulem} +\usepackage[mathlines]{lineno}% Enable numbering of text and display math +\linenumbers\relax % Commence numbering lines +% The amssymb package provides various useful mathematical symbols +\usepackage{amssymb} +% The amsthm package provides various useful mathematical fonts +\usepackage{amsfonts} +% Various mathematical packages +\PassOptionsToPackage{cmex10draft}{graphics} +\usepackage[standard]{ntheorem} +% Various packages +\usepackage{stmaryrd} % llbracket +\usepackage{dsfont} % mathds +\usepackage{multirow} % tables + +\newcommand{\ie}{\textit{i.e.}} +\newcommand{\Nats}[0]{\ensuremath{\mathds{N}}} +\newcommand{\R}[0]{\ensuremath{\mathds{R}}} +\newcommand{\Bool}[0]{\ensuremath{\mathds{B}}} + +\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} + +%%\pacs{Valid PACS appear here}% PACS, the Physics and Astronomy + % Classification Scheme. +%%\keywords{Suggested keywords}%Use showkeys class option if keyword + %display desired +\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 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 used chaotic neural networks +these last years. This interest comes from their complex dynamics and +the various potential application areas. Chaotic neural networks are +particularly considered to build 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 first 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 +computer security field, the use of chaotic dynamics is motivated by +their unpredictability and random-like behaviors. Indeed, +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} are frequently used to build +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 networks are basically +used 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 using 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 purpose 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 an equivalence between chaotic +iterations and a class of globally recurrent multilayer perceptrons. +We investigate the converse problem too, that is, the ability for +classical MultiLayer Perceptrons (MLP) 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 the 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 will +experiment several MLPs and try to learn some iterations of this kind. +We will 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 in the +presence of chaos, 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. The following two~sections are +devoted to the dual case: checking whether an existing neural network +is chaotic or not (Section \ref{S3}), and discussion on topological +properties of chaotic neural networks (Section~\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 presented. 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 recalled 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 if initially the differences are 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 $k>0$ such that +$f^k(U) \cap V \neq \emptyset$. +\end{definition} + +This property implies that a dynamical system cannot be broken into +simpler subsystems. It is intrinsically complicated and cannot be +simplified. 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 $\mathcal{X}$ ($\forall x \in + \mathcal{X}$, 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 $. $\delta$ 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 $N(i,x)$ iff $f_i(x)$ 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. 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 +measure 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 $f_1\left(x_1,\dots,x_n\right)=\left( +\overline{x_1},x_1,x_2,\dots,x_\mathsf{n}\right)$. 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 if 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 or not. 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: as inputs +it has $n$ binary digits and one integer value, while it produces $n$ +bits. Moreover, each binary output is connected with a feedback +connection to an input one. + +\begin{itemize} +\item At initialization, the network is feeded 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 $U$,$V \neq +\emptyset$, $n_0 \in \mathds{N}$ can be found such that $\forall n +\geq n_0$, $f^n(U) \cap V \neq \emptyset$. +\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 presented previously, 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. + +Now, what are 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 +$$d_n(x,y)=\max\{d(f^i(x),f^i(y)): 0\leq i 0$ and $n \geqslant 1$, two points of $M$ are +$\varepsilon$-close with respect to this metric if their first $n$ +iterates are $\varepsilon$-close. + +This metric allows one to distinguish in a neighborhood of an orbit +the points that move away from each other during the iteration from +the points that travel together. A subset $E$ of $M$ is said to be +$(n, \varepsilon)$-separated if each pair of distinct points of $E$ is +at least $\varepsilon$ apart in the metric $d_n$. Denote by $H(n, +\varepsilon)$ the maximum cardinality of an $(n, +\varepsilon)$-separated set, +\begin{definition} +The {\it topological entropy} of the map $f$ is defined by (see e.g., +Ref.~\cite{Adler65} or Ref.~\cite{Bowen}) +$$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 iteration 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. + +Secondly, how do we 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, 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 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. + +\end{document} +% +% ****** End of file chaos-paper.tex ****** -- 2.39.5