From dd837d463f86ebb399eb1cac274139e98815fdd1 Mon Sep 17 00:00:00 2001 From: couchot Date: Mon, 15 Jun 2015 22:57:05 +0200 Subject: [PATCH] debut chaosperceptron --- 12TIPE.tex | 5 +- annexePromelaProof.tex | 17 +- chaosANN.tex | 588 ++++++++++++++++++++++++++++++++++ images/chaotic_trace2.pdf | Bin 0 -> 28026 bytes images/non-chaotic_trace2.pdf | Bin 0 -> 22953 bytes images/perceptron.pdf | Bin 0 -> 12346 bytes images/scheme.odg | Bin 0 -> 10791 bytes images/scheme.pdf | Bin 0 -> 27531 bytes main.pdf | Bin 1884364 -> 1980602 bytes main.tex | 16 +- 10 files changed, 607 insertions(+), 19 deletions(-) create mode 100644 chaosANN.tex create mode 100644 images/chaotic_trace2.pdf create mode 100644 images/non-chaotic_trace2.pdf create mode 100644 images/perceptron.pdf create mode 100644 images/scheme.odg create mode 100644 images/scheme.pdf diff --git a/12TIPE.tex b/12TIPE.tex index 4f9fa9b..4cbc26d 100644 --- a/12TIPE.tex +++ b/12TIPE.tex @@ -33,9 +33,10 @@ $\mathcal{X}_u = et la fonction d'iteration $G_{f_u}$ définie de $\mathcal{X}_u$ dans lui-même par -\[ +\begin{equation} G_{f_u}(x,s)=(F_{f_u}(x,s_0),\sigma(s)). -\] +\label{eq:sch:unaire} +\end{equation} Dans cette définition, la fonction $\sigma: \llbracket1;{\mathsf{N}}\rrbracket^{\Nats} \longrightarrow diff --git a/annexePromelaProof.tex b/annexePromelaProof.tex index d56d89b..70981a9 100644 --- a/annexePromelaProof.tex +++ b/annexePromelaProof.tex @@ -140,7 +140,7 @@ les deux cas sont égaux à Pour le dernier item, soit $k$, $0 \le k \le n-1$. A la fin de la première exécution du processus \verb+update_elems+, la valur de -\ver+Xp[k]+ est $F(\verb+Xd[+k\verb+].v[0]+, \ldots, +\verb+Xp[k]+ est $F(\verb+Xd[+k\verb+].v[0]+, \ldots, \verb+Xd[+k\verb+].v[+n-1\verb+]+)$. Ainsi par définition de $Xd$, ceci est égal à $F(Xd^1_{k\,0}, \ldots,Xd^1_{k\,n-1})$. Grâce à l'équation \Equ{eq:correct_retrieve}, @@ -148,15 +148,16 @@ on peut conclure la preuve. -\paragraph{Inductive case:} +\paragraph{Induction:} +Supposons maintenant que le lemme~\ref{lemma:execution} est établi jusqu'à +l'itération $l$. -Suppose now that lemma~\ref{lemma:execution} is established until iteration $l$. +Tout d'abord, si le domaine de définition de la fonction $M_{ij}^l$ +n'est pas vide, par hypothèse d'induction $M_{ij}^{l}(0)$ est +$\left(X_i^{D_{ji}^{c}}, D_{ji}^{c},c +\right)$ où $c$ est $\min\{k | D_{ji}^k > D_{ji}^{l-2} \}$. -First, if domain of definition of the function $M_{ij}^l$ is not empty, by -induction hypothesis $M_{ij}^{l}(0)$ is $\left(X_i^{D_{ji}^{c}}, D_{ji}^{c},c -\right)$ where $c$ is $\min\{k | D_{ji}^k > D_{ji}^{l-2} \}$. - -At iteration $l$, if $l < c + 1$ then the \verb+skip+ statement is executed in +A l'itération $l$, si $l < c + 1$ alors \verb+skip+ statement is executed in the \verb+fetch_values+ function. Thus, $M_{ij}^{l+1}(0)$ is equal to $M_{ij}^{l}(0)$. Since $c > l-1$ then $D_{ji}^c > D_{ji}^{l-1}$ and hence, $c$ is $\min\{k | D_{ji}^k > D_{ji}^{l-1} \}$. Obviously, this implies also that diff --git a/chaosANN.tex b/chaosANN.tex new file mode 100644 index 0000000..1754b80 --- /dev/null +++ b/chaosANN.tex @@ -0,0 +1,588 @@ +% \section{Introduction} +% \label{S1} + +Les réseaux de neurones chaotiques ont été étudiés à de maintes reprises +par le passé en raison notamment de leurs applications potentielles: +%les mémoires associatives~\cite{Crook2007267} +les composants utils à la sécurité comme les fonctions de +hachage~\cite{Xiao10}, +le tatouage numérique~\cite{1309431,Zhang2005759} +ou les schémas de chiffrement~\cite{Lian20091296}. +Dans tous ces cas, l'emploi de fonctions chaotiques est motivé par +leur comportement imprévisibile et proche de l'aléa. + + +Les réseaux de neurones chaotiques peuvent être conçus selon plusieurs +principes. Des neurones modifiant leur état en suivant une fonction non +linéaire son par exemple appelés neurones chaotiques~\cite{Crook2007267}. +L'architecture de réseaux de neurones de type Perceptron multi-couches +(MLP) n'iterent quant à eux, pas nécesssairement de fonctions chaotiques. +Il a cependant été démontré que ce sont des approximateurs +universels~\cite{Cybenko89,DBLP:journals/nn/HornikSW89}. +Ils permettent, dans certains cas, de simuler des comportements +physiques chaotiques comme le circuit de Chua~\cite{dalkiran10}. +Parfois~\cite{springerlink:10.1007/s00521-010-0432-2}, +la fonction de transfert de cette famille de réseau celle +d'initialisation sont toutes les deux définies à l'aide de +fonctions chaotiques. + + + + + +Ces réseaux de neurones partagent le fait qu'ils sont qualifiés de +``chaotiques'' sous prétexte qu'ils embarquent une fonction de ce type +et ce sans aucune preuve rigoureuse. Ce chapitre caractérise la +classe des réseaux de neurones MLP chaotiques. Il +s'intéresse ensuite à l'étude de prévisibilité de systèmes dynamiques +discrets chaotiques par cette famille de MLP. + +\JFC{revoir plan} + +The remainder of this research work is organized as follows. The next +section is devoted to the basics of Devaney's chaos. Section~\ref{S2} +formally describes how to build a neural network that operates +chaotically. Section~\ref{S3} is devoted to the dual case of checking +whether an existing neural network 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{Un réseau de neurones chaotique au sens de Devaney} +\label{S2} + +On considère une fonction +$f:\Bool^n\to\Bool^n$ telle que $\textsc{giu}(f)$ est fortement connexe. +Ainsi $G_{f_u}$ est chaotique d'après le théorème~\ref{Th:CaracIC}. + +On considère ici le schéma unaire défini par l'équation (\ref{eq:asyn}). +On construit un perceptron multi-couche associé à la fonction +$F_{f_u}$. +Plus précisément, pour chaque entrée + $(x,s) \in \mathds{B}^n \times [n]$, +la couche de sortie doit générer $F_{f_u}(x,s)$. +On peut ainsi lier la couche de sortie avec celle d'entrée pour représenter +les dépendance entre deux itérations successives. +On obtient une réseau de neurones dont le comportement est le suivant +(voir Figure.~\ref{Fig:perceptron}): + +\begin{itemize} +\item Le réseau est initialisé avec le vecteur d'entrée + $\left(x^0,S^0\right) \mathds{B}^n \times [n]$ + et calcule le vecteur de sortie + $x^1=F_{f_u}\left(x^0,S^0\right)$. + Ce vecteur est publié comme une sortie et est aussi retournée sur la couche d'entrée + à travers les liens de retours. +\item Lorsque le réseau est activé à la $t^{th}$ itération, l'état du + système $x^t \in \mathds{B}^n$ reçu depuis la couche de sortie ainsi que le + premier terme de la sequence $(S^t)^{t \in \Nats}$ + (\textit{i.e.}, $S^0 \in [n]$) servent à construire le nouveau vecteur de sortie. + Ce nouveau vecteur, qui représente le nouvel état du système dynamique, satisfait: + \begin{equation} + x^{t+1}=F_{f_u}(x^t,S^0) \in \mathds{B}^n \enspace . + \end{equation} +\end{itemize} + +\begin{figure} + \centering + \includegraphics[scale=0.625]{images/perceptron} + \caption{Un perceptron équivalent aux itérations unitaires} + \label{Fig:perceptron} +\end{figure} + +Le comportement de ce réseau de neurones est tel que lorsque l'état +initial est composé de $x^0~\in~\mathds{B}^n$ et d'une séquence + $(S^t)^{t \in \Nats}$, alors la séquence contenant les vecteurs successifs +publiés $\left(x^t\right)^{t \in \mathds{N}^{\ast}}$ est exactement celle produite +par les itérations unaires décrites à la section~\ref{sec:TIPE12}. +Mathématiquement, cela signifie que si on utilise les mêmes vecteurs d'entrées +les deux approches génèrent successivement les mêmes sorties. +En d'autres termes ce réseau de neurones modélise le comportement de +$G_{f_u}$, dont les itérations sont chaotiques sur $\mathcal{X}_u$. +On peut donc le qualifier de chaotique au sens de Devaney. + +\section{Vérifier si un réseau de neurones est chaotique} +\label{S3} +On s'intéresse maintenant au cas où l'on dispose d'un +réseau de neurones de type perceptron multi-couches +dont on cherche à savoir s'il est chaotique (parce qu'il a par exemple été +déclaré comme tel) au sens de Devaney. +On considère de plus que sa topologie est la suivante: +l'entrée est constituée de $n$ bits et un entier, la sortie est constituée de $n$ bits +et chaque sortie est liée à une entrée par une boucle. + +\begin{itemize} +\item Le réseau est initialisé avec $n$~bits + $\left(x^0_1,\dots,x^0_n\right)$ et une valeur entière $S^0 \in [n]$. +\item A l'itération~$t$, le vecteur + $\left(x^t_1,\dots,x^t_n\right)$ permet de construire les $n$~bits + servant de sortie $\left(x^{t+1}_1,\dots,x^{t+1}_n\right)$. +\end{itemize} + +Le comportement de ce type de réseau de neurones peut être prouvé comme +étant chaotique en suivant la démarche énoncée maintenant. +On nomme tout d'abord $F: \mathds{B}^n \times [n] \rightarrow +\mathds{B}^n$ la fonction qui associe +au vecteur +$\left(\left(x_1,\dots,x_n\right),s\right) \in \mathds{B}^n \times[n]$ +le vecteur +$\left(y_1,\dots,y_n\right) \in \mathds{B}^n$, où +$\left(y_1,\dots,y_n\right)$ sont les sorties du réseau neuronal +àaprès l'initialisation de la couche d'entrée avec +$\left(s,\left(x_1,\dots, x_n\right)\right)$. Ensuite, on définie $f: +\mathds{B}^n \rightarrow \mathds{B}^n$ telle que +$f\left(x_1,x_2,\dots,x_n\right)$ est égal à +\begin{equation} +\left(F\left(\left(x_1,x_2,\dots,x_n\right),1\right),\dots, + F\left(\left(x_1,x_2,\dots,x_n\right)\right),n\right) \enspace . +\end{equation} +Ainsi pour chaque $j$, $1 \le j \le n$, on a +$f_j\left(x_1,x_2,\dots,x_n\right) = +F\left(\left(x_1,x_2,\dots,x_n\right),j\right)$. +Si ce réseau de neurones est initialisé avec +$\left(x_1^0,\dots,x_n^0\right)$ et $S \in [n]^{\mathds{N}}$, +il produit exactement les même sorties que les itérations de $F_{f_u}$ avec une +condition initiale $\left((x_1^0,\dots, x_n^0),S\right) \in \mathds{B}^n \times [n]^{\mathds{N}}$. +Les itérations de $F_{f_u}$ +sont donc un modèle formel de ce genre de réseau de neurones. +Pour vérifier s'il est chaotique, il suffit ainsi +de vérifier si le graphe d'itérations +$\textsc{giu}(f)$ est fortement connexe. + + +\section{Suitability of Feedforward Neural Networks +for Predicting Chaotic and Non-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 such neural networks 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 peceptron. + +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. The remaining of this section shows how to translate +iterations of such functions into a model amenable to be learned by an +ANN. 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 close in a +decimal ordering, but their Hamming distance is 5. This is why Gray +codes~\cite{Gray47} have been preferred. + +Secondly, let us 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 +\cite{Cybenko89,DBLP:journals/nn/HornikSW89}. 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 back-propagation, can learn successfully the dynamics of +Chua's circuit. + +In these experiments we consider MLPs having one hidden layer of +sigmoidal neurons and output neurons with a linear activation +function. They are trained using the Limited-memory +Broyden-Fletcher-Goldfarb-Shanno quasi-newton algorithm in combination +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. Such a rate +represents the percentage of input-output pairs belonging to the test +subset for which the corresponding output value was correctly +predicted. 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 these feedforward 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 feedforward 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. +Figures~\ref{Fig:chaotic_predictions} and +\ref{Fig:non-chaotic_predictions} present the predictions given by two +feedforward multilayer perceptrons that were respectively trained to +learn chaotic and non-chaotic data, using the second coding scheme. +Each figure shows for each sample of the test subset (577~samples, +representing 25\% of the 2304~samples) the configuration that should +have been predicted and the one given by the multilayer perceptron. It +can be seen that for the chaotic data the predictions are far away +from the expected configurations. Obviously, the better predictions +for the non-chaotic data reflect their regularity. + +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} + +\begin{figure} + \centering + \includegraphics[scale=0.5]{images/chaotic_trace2} + \caption {Second coding scheme - Predictions obtained for a chaotic test subset.} + \label{Fig:chaotic_predictions} +\end{figure} + +\begin{figure} + \centering + \includegraphics[scale=0.5]{images/non-chaotic_trace2} + \caption{Second coding scheme - Predictions obtained for a non-chaotic test subset.} + \label{Fig:non-chaotic_predictions} +\end{figure} + +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 +considered feedforward 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. Secondly, 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. + +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 then be stated. + +% \appendix{} + +% \begin{Def} \label{def2} +% A continuous function $f$ on a topological space $(\mathcal{X},\tau)$ +% is defined to be {\emph{topologically transitive}} if for any pair of +% open sets $U$, $V \in \mathcal{X}$ there exists +% $k \in +% \mathds{N}^{\ast}$ +% such that +% $f^k(U) \cap V \neq \emptyset$. +% \end{Def} + +%\bibliography{chaos-paper}% Produces the bibliography via BibTeX. + +%\end{document} +% +% ****** End of file chaos-paper.tex ****** diff --git a/images/chaotic_trace2.pdf b/images/chaotic_trace2.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2a6b0d5e49ae344462ce38508a14fe7ae6b5972c GIT binary patch literal 28026 zcmV)IK)k;tP((&8F)lO;CCBWKq6#%2Fd%PYY6?6&FHB`_XLM*FHXtw{QZGhnY;H@GM`Cu>w%%o7#QjH zPVmBRJw`@d%d%t$^X(@y8IN;{>zX2^FRMcKmYtk%kx*hDYe)4-|vss$1nf>{p0ha=Qe*0w(GTJO)QNcxQ;Igg5jT>om3%GG|&TtuX^mTH^nmf1B( zzDa|O$217I{?#JYR<}6itRYZnrL6L6m;FzOY?StPkbj*x_4<`(Ew>miNYLh7Qh$EM zB{GUhWE7LgDgPFUa(ZfLkVH9wOlv*DpiD|}nUvx(8Rg$3)2-8auuKJHD&uCLmNSaU zWE7LhDgPFk%GH0hMIu!YsrFHMyYl2Kg0mE!ts zlz)pzt(0GjYpH`se`%ac<-~Yq^_@_r?)5e~E&D4Lk zo#|oz{MYaQ{QWooDM2B6d}$SmpoxRre1ecM~p4#q|~#Q-0DZ>;CH6xn_EQ&Lx_8*F$do`Ndhb9V1_LS3xe)gJ;`6sOkNMwXZ8w z>6E{Kvd6?V#>ShJxOTyuvJ$GpN?tW9*8sgrRL)fsezl;St8ICK(u@DVRe4xg7i!a* z32S2Q=?A1)QNQkq_nkIfTW`v)?a^N==%JKE4CS5uy%0Io%ng`RD`fiJgc*NlDa^F7+)_1Xn{F{*v7nkt*;NUY`U(ThS{k)->8~c3wB>s15;sNWDgo?P{VcpW-LEOH!g$CkXqx=elx833AuSq|@?6*hS zUE3C+dE{NoNa1Nek3wtNhf*7_W%FnWyZ`8MR2n_1>$rAb0-mu|1Sl(Z?Xj>fXW}_J z&s;+vm^oMo_nM4ko%OA69bIOlv1q~+a4%J8Ae#91WbK`6*Xy1R@{+MW?T+(O_HrC3 z`TYwdT_p}ltKtls*E`nx7mu5(SD3u>l5u^1ewgqq<0gwdVSh!}c=CAjiIkJ>Jx&vVk?ogkl z>@6l|X`%bvl#8a`dqPX}wI?2LlCt(3=#-j5HPu9FclX{PMy=sTYeReD{(Lpfllz2j zR_aByR$rSQes0qEe1E?Z?=D-~o+IolNYX0*bZcS!xM%amrD+$mTDw@77AJ?&;^c5s zSc!VFK6)?pXc#HI(v)|9Q`PiFRnu|mxDAi&LuKjjwBg0*ySD&gkuF~~O!4JvIK`Js zjD29}QCF_({ZU}q_P!6lHznAj_rw@%l$&z#(yELUd-&KRKja#FUgv7}W#6E(=(W4QdtEI754S=sKr4ZqN*XRm~IQRU4MD`t)hrh_sk zZ_<~Qdl&##3FFFo2VvxBVH5whMyc-C9&df%xsB5tDTRmjn=swRsi{`ni+(7rTVfCE zkK5D@CR5GP9=<8TSg-f8;uo{*5z{A%n-Zp%np>D&Zpy_oSi~ zP%~@Y|Qhlv;3x9K}XvN25xOL>><>)&`Pd7+UGYVY|zx6`LwC_67~^p{Kkg#=b%0N(UWn} zvaZ)2JoT&KJaexHVV=3C=cw2h7|r)bSf12to6(w1^Fh7#ma6@f*wM}~N}|sirlFg% zTf&PrT2$fDgYi@a5p!*mmnbs~AT#4y@&4Qcz6&!;9F}Ga6JDL28e(2y@2IYAy&@%t z&BiJvkxE@9R{GjQE{L|88ya=VpbN=8j(h7GzZhY}VQ;O(pTX>IDFqv$r9Djhrg17^ zc)2ON%7CYGmKJ)+-R;Aa(b5&V$xQ(rZ}M~cHA9zgX^-EodM&@>Hxa`4-Exa!qWyXk|XbhgKZ<8SGrM0obT-|0$$S`x*Nx3Q9RI}0{ z%KaT?m^S0+-sT8*z>%{@019p|y&WESFSB9nPeADW z!{0w~e*V$wc9i`MFc)XKS*{jN@jrI54>)qv4?xi$WF*E=Ux2ay^(Gi{)~e0;4B&U_ zgbl8bIuE!6;!NNINY4WaW<2u!xXXb{Arz8g*>B$r%hx*}t7R8NtKB5&wDSM{xT1;n zG0%p*(ERH6v-`Lac7Ico;%nXwZe)D6H-wET+c(0;6Kx_NQ?3%-LO$+8;q@UDtdDM$ zag~c&MH}%T?cr-DL;6L-1*zCMbE#h3K2GiG1QshK>&;4<9;I} z)-?fi%k+I+Lo9o~8MD^#<8_jU>Vfnmo#&p6J@`C)tW_LD2mmw^f{d`O^RY^G40U*M z4Bia7S>Dl>QC>4vb#i~2N=jz}m7J`kFGOR#mTv=DhhDE!ugw?7P=uf`(xDt>q=0ZU zdR;sxzE%&~xLy(JrbAUB?mi~L{x$)mi9mvqsd_euRGFj5#9%PA^ZgZfAi~f}HVh#c z201d_5H*~Le#}+LAojN*v>F;^wCD`=0rw`ErHufyv^2NR(vJrd&eB@fHhP+ml#QNG zbNSdg*z<8?@JygiRe%s@f^nTQfw*1`u6ykB+FZNSk1=WBF?TJkRwY=y&cZD-Yit>9 zEQg@(5_{O!JiZ9z@e(tH`@^z_zV-bv6YK5HE*7g9Pwr^u2GrBT+;~#QGO(SriUCF` z2$Lz}jg)zjf3s{42#49<2qJwAP)c-O{8)P-(md?bL7NO;b%`S&^|Esy^Sc9}N*QmY zmKf%B&0E?rp0Bpd!Bo4PE}OpQedi0Qk2~~9AJlK?g4TG}2h~##`=L1k%AWMsDYDDH z+tmFLZjOsi(RRxr!23b-|An_#iqmG+d&h@_L$yv5YyY+UT+YA4%d1UHFz~`-Hmt&F)HJV1N zo~Dtyh&qV+j5<(5(@4BN5yPBHZJ<{Q4)xMrd!$Fl(2x3|LiRXMXhUgko+s3I)e2$G ztX_z#aEn|uwut+?O4m06^nGb1@A@lr`|TsE2+s%QW(Vf4xxErZ<2AB6+S=@^ZRE4B zddK4ItM0gf6W7Sn$q*;5k)@@)ZF*chpilAFymGB=E?yq18?ecsZ3T{{DTn?0k2?#y zAlJEsSQ(ZOukWvkq3*>tei#J?kf*)Wn=6s0*hf8*UB|>j*;FicuNNa627j!j9B0LK zMCf8)bxEepJavAy#i_eP`vP@0mM>3p>cMZ|<4)7hQQQ}NsFSg1f75;FuX#ZG&h$ro ztwEl`t7UC46lXhRusG_XEq%xc(2o}Np&KTgUVXr?+hBx%Fro8(%~PA9_I^5)OgzM) zY|@BWwiS-0KJH15Q1fWCHA17Uc{ZAQpf8hLC}8nuTe`*bI+CNgBaZ9)u?hZ6e%$i^xU;)zkNUnRY|(_Oqu%SJI_eOPYoZveBg$U-)V4J zghH~wN!G)%&<*3`%?85A

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+\usepackage{multirow} %\declaretheorem{theorem} %%-------------------- @@ -141,18 +142,14 @@ Blabla blabla. \part{Réseaux Discrets} - - \chapter{Iterations discrètes de réseaux booléens} \JFC{chapeau à refaire} \section{Formalisation} \input{sdd} - \section{Combinaisons synchrones et asynchrones} \input{mixage} - \section{Conclusion} \JFC{Conclusion à refaire} @@ -165,7 +162,7 @@ de l'asynchronisme en terme de vitesse de convergence. -\chapter[Preuve de convergence de systèmes booléens]{Preuve automatique de convergence}\label{chap:promela} +\chapter{Preuve automatique de convergence}\label{chap:promela} \input{modelchecking} @@ -176,7 +173,8 @@ de l'asynchronisme en terme de vitesse de convergence. \part{Des systèmes dynamiques discrets au chaos} -\chapter{Characterisation des systèmes +\chapter[Caracterisation des systèmes + discrets chaotiques]{Caracterisation des systèmes discrets chaotiques pour les schémas unaires et généralisés} La première section rappelle ce que sont les systèmes dynamiques chaotiques. @@ -190,7 +188,7 @@ On montre qu'on a des résultats similaires. \label{subsec:Devaney} \input{devaney} -\section{Schéma unaire} +\section{Schéma unaire}\label{sec:TIPE12} \input{12TIPE} \section{Schéma généralisé} @@ -203,7 +201,7 @@ On montre qu'on a des résultats similaires. \chapter{Prédiction des systèmes chaotiques} -13 JournalMichel +\input{chaosANN} @@ -214,7 +212,7 @@ On montre qu'on a des résultats similaires. - \part{Conclusion et Perspectives} +\part{Conclusion et Perspectives} \JFC{Perspectives pour SDD->Promela} Among drawbacks of the method, one can argue that bounded delays is only -- 2.39.5