350 rub
Journal Neurocomputers №12 for 2013 г.
Article in number:
Solving the problems of cluster analysis based on the oscillatory neural networks
Keywords: model Kuramoto synchronization data clustering graph coloring
Authors:
A. V. Novikov - Post-graduate Student, Department Computer Systems and Software Technologies, St.-Petersburg State Polytechnical University. E-mail: spb.andr@yandex.ru
E. N. Benderskaya - Ph.D. (Eng.), Associate Professor, Department Computer Systems and Software Technologies, St.-Petersburg State Polytechnical University. E-mail: helen.bend@gmail.com
Abstract:
The first section of the article presents research of synchronization processes in oscillatory neural networks with different structures, estimates of the local and global synchronization of oscillators which are based on the Kuramoto model. The analyses of the main parameters that determine the synchronization of oscillators in the network are also described. The second part describes practical ways to use oscillatory networks for solving the problems of clustering of N-dimensional data and also ways to use the networks in question for solving the problem of graph coloring. There are practical recommendations for setting networks to achieve the most accurate solutions. The article presents the analysis of the main issues and use limitations of the oscillator networks.
Pages: 31-36
References

  1. Benderskaya E.N., Zhukova S.V. Osczillyatorny'e nejronny'e seti s xaoticheskoj dinamikoj v zadachax klasternogo analiza // Nejrokomp'yutery': razrabotka, primenenie. 2011 № 7. S. 74-86
  2. Benderskaya E.N. Vozmozhnosti primeneniya nekotory'x xarakteristik sinxronizaczii dlya vy'yavleniya samoorganizuyushhixsya klasterov v osczillyatornoj nejronnoj seti s xaoticheskoj dinamikoj // Nejrokomp'yutery': razrabotka, primenenie. 2012. № 11. S. 69-73.
  3. Benderskaya E.N. Perspektivny'e konczepczii razrabotki intellektual'ny'x sistem // Nauchno-texnicheskie vedomosti Sankt-Peterburgskogo gosudarstvennogo politexnicheskogo universiteta. Informatika. Telekommunikaczii. Upravlenie. 2011. T. 6-1. № 138. S. 173-181.
  4. Savel'ev A.V. Rasshirenie ponyatiya nejrokomp'yutera i nejrokomp'yutinga // Nejrokomp'yutery': razrabotka, primenenie. 2013. № 7. S. 58-68.
  5. Bohm C., Plant C., Shao J., Yang Q. Clustering by synchronization // KDD - 10 Proceeding of the 16th ACM SIGKDD international conference of Knowledge discovery and data mining. 2010. R. 583-592.
  6. Cumin D., Unsworth C.P. Generalizing the Kuramoto Model for the Study of Neuronal Synchronisation in the Brain // Report University of Auckland School of Engineering. 2006. 638 r.
  7. Daido H. Quasientrainment and slow relaxation in a population of oscillators with random and frustrated interactions // Phys. Rev. Lett. 73. 1992. R. 1073-1076.
  8. Kuramoto Y. Chemical Oscillations Waves, and Turbulence / Springer-Verlag Berlin Neidelberg New York Tokyo. 1984. 157 p.
  9. Ultsch A. Clustering with SOM: U*C // Workshop on Self Organizing Feature Maps. 2005. R. 31-37.
  10. Wang X., Jiao L., Wu J. Extracting hierarchical organization of complex networks by dynamics towards synchronization // Physica A. 2009. 388. R. 2975-2986.
  11. Wu J., Jiao L., Chen W. Clustering dynamics of nonlinear oscillator network: Application to graph coloring problem // Physica D. 2011. 240. R. 1972-1978.