Conclusion raccourcie et biblio corrigee

diff --git a/chaos-paper.bib b/chaos-paper.bib
index 120e1fe089e08f212c89cf573bd749419c452298..bdc8bb1eec02703edf83dbdb99fe8360498367c5 100644 (file)
--- a/chaos-paper.bib
+++ b/chaos-paper.bib
@@ -285,7 +285,7 @@ doi={10.1109/IJCNN.2008.4634274}
 }\r
 \r
 @INPROCEEDINGS{guyeux10ter,\r
 }\r
 \r
 @INPROCEEDINGS{guyeux10ter,\r

-  author = {Bahi, Jacques and Guyeux, Christophe},\r
+  author = {Bahi, Jacques M. and Guyeux, Christophe},\r

   title = {A new chaos-based watermarking algorithm},\r
   booktitle = {SECRYPT'10, International Conference on Security \r
 and Cryptography},\r
   title = {A new chaos-based watermarking algorithm},\r
   booktitle = {SECRYPT'10, International Conference on Security \r
 and Cryptography},\r


@@ -348,7 +348,7 @@ and Cryptography},
 }\r
 \r
 @INPROCEEDINGS{gfb10:ip,\r
 }\r
 \r
 @INPROCEEDINGS{gfb10:ip,\r

-  author = {Guyeux, Christophe and Friot, Nicolas and Bahi, Jacques},\r
+  author = {Guyeux, Christophe and Friot, Nicolas and Bahi, Jacques M.},\r

   title = {Chaotic iterations versus Spread-spectrum: chaos and stego security},\r
   booktitle = {IIH-MSP'10, 6-th International Conference on Intelligent Information Hiding and\r
         Multimedia Signal Processing},\r
   title = {Chaotic iterations versus Spread-spectrum: chaos and stego security},\r
   booktitle = {IIH-MSP'10, 6-th International Conference on Intelligent Information Hiding and\r
         Multimedia Signal Processing},\r


@@ -414,10 +414,9 @@ inhal = {no},
 domainehal = {INFO:INFO_DC, INFO:INFO_CR, INFO:INFO_MO},\r
 equipe = {and},\r
 classement = {ACTI},\r
 domainehal = {INFO:INFO_DC, INFO:INFO_CR, INFO:INFO_MO},\r
 equipe = {and},\r
 classement = {ACTI},\r

-author = {Bahi, Jacques and Guyeux, Christophe and Salomon, Michel},\r
+author = {Bahi, Jacques M. and Guyeux, Christophe and Salomon, Michel},\r

 title = {Building a Chaotic Proven Neural Network},\r
 booktitle = {ICCANS 2011, IEEE Int. Conf. on Computer Applications and Network Security},\r
 title = {Building a Chaotic Proven Neural Network},\r
 booktitle = {ICCANS 2011, IEEE Int. Conf. on Computer Applications and Network Security},\r

-pages = {***--***},\r

 address = {Maldives, Maldives},\r
 month = may,\r
 year = 2011,\r
 address = {Maldives, Maldives},\r
 month = may,\r
 year = 2011,\r

diff --git a/main.tex b/main.tex
index de92eaacff17ac2a6c6c4cc2f0a0e968b09fb36e..0ce8df055c645140ec18bcc3e3343b0188ab037c 100644 (file)
--- a/main.tex
+++ b/main.tex
@@ -1072,13 +1072,13 @@ Chaotic/non chaotic & \multicolumn{3}{c|}{Output = Strategy} \\
 
 In  this paper,  we have  established an  equivalence  between chaotic
 iterations,  according to  the Devaney's  definition of  chaos,  and a
 
 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
+class  of multilayer  perceptron  neural networks.   Firstly, we  have

 described how to build a neural network that can be trained to learn a
 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
+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
 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


@@ -1092,10 +1092,7 @@ 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
 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.
+information  hiding  schemes.

 
 In  future  work we  intend  to  enlarge  the comparison  between  the
 learning   of  truly   chaotic  and   non-chaotic   behaviors.   Other
 
 In  future  work we  intend  to  enlarge  the comparison  between  the
 learning   of  truly   chaotic  and   non-chaotic   behaviors.   Other


@@ -1104,8 +1101,7 @@ 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
 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.
+will be  stated.

 
 % \appendix{}
 
 
 % \appendix{}