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Complexity
Volume 2017, Article ID 4978613, 8 pages
https://doi.org/10.1155/2017/4978613
Research Article

A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

1College of Electrical Engineering, Northwest Minzu University, Lanzhou, China
2Key Laboratory of National Language Intelligent Processing, Gansu Province, China
3National University of Sciences and Technology, Islamabad, Pakistan

Correspondence should be addressed to Ning Cai; nc.gro.auhgnist@19gniniac

Received 18 February 2017; Accepted 26 March 2017; Published 13 September 2017

Academic Editor: Zeraoulia Elhadj

Copyright © 2017 Ning Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Cangelosi and D. Parisi, Eds., Simulating the Evolution of Language, Springer, London, UK, 2002.
  2. M. H. Edwards, D. E. Robinson, K. A. Ward et al., “Cluster analysis of bone microarchitecture from high resolution peripheral quantitative computed tomography demonstrates two separate phenotypes associated with high fracture risk in men and women,” Bone, vol. 88, pp. 131–137, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Liu, C. Wang, and Y. Jing, “Estimating the optimal number of communities by cluster analysis,” International Journal of Modern Physics B, vol. 30, no. 8, 1650037, 10 pages, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  4. L. Dong, L. Wang, S. F. Khahro, S. Gao, and X. Liao, “Wind power day-ahead prediction with cluster analysis of NWP,” Renewable and Sustainable Energy Reviews, vol. 60, pp. 1206–1212, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Z. Arifin and A. Asano, “Image segmentation by histogram thresholding using hierarchical cluster analysis,” Pattern Recognition Letters, vol. 27, no. 13, pp. 1515–1521, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1990. View at Publisher · View at Google Scholar · View at MathSciNet
  9. C. Ding, X. He, H. Zha, M. Gu, and H. Simon, “A min-max cut algorithm for graph partitioning and data clustering,” in Proceedings of the IEEE International Conference on Data Mining (ICDM '01), pp. 107–114, San Jose, Calif, USA, 2001. View at Publisher · View at Google Scholar
  10. D. G. Corneil and C. C. Gotlieb, “An efficient algorithm for graph isomorphism,” Journal of the Association for Computing Machinery, vol. 17, pp. 51–64, 1970. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 23, pp. 8577–8582, 2006. View at Publisher · View at Google Scholar
  12. F. R. Chung, Spectral Graph Theory, CBMS Regional Conference Series in Mathematics, American Mathematical Society, 1997. View at MathSciNet
  13. S. M. von Dongen, Graph clustering by flow simulation [Doctoral dissertation], University of Utrecht, 2000.
  14. A. Arenas, A. Díaz-Guilera, and C. J. Pérez-Vicente, “Synchronization reveals topological scales in complex networks,” Physical Review Letters, vol. 96, no. 11, Article ID 114102, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Yu and L. Wang, “Group consensus in multi-agent systems with switching topologies and communication delays,” Systems & Control Letters, vol. 59, no. 6, pp. 340–348, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  16. H.-X. Hu, L. Yu, W.-A. Zhang, and H. Song, “Group consensus in multi-agent systems with hybrid protocol,” Journal of the Franklin Institute. Engineering and Applied Mathematics, vol. 350, no. 3, pp. 575–597, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  17. H.-X. Hu, W. Yu, Q. Xuan, C.-G. Zhang, and G. Xie, “Group consensus for heterogeneous multi-agent systems with parametric uncertainties,” Neurocomputing, vol. 142, pp. 383–392, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Su, Z. Rong, M. Z. Q. Chen, X. Wang, G. Chen, and H. Wang, “Decentralized adaptive pinning control for cluster synchronization of complex dynamical networks,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 43, no. 1, pp. 394–399, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Su, M. Z. Chen, X. Wang, H. Wang, and N. V. Valeyev, “Adaptive cluster synchronisation of coupled harmonic oscillators with multiple leaders,” IET Control Theory & Applications, vol. 7, no. 5, pp. 765–772, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  20. D. Xie, Q. Liu, L. Lv, and S. Li, “Necessary and sufficient condition for the group consensus of multi-agent systems,” Applied Mathematics and Computation, vol. 243, pp. 870–878, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. H. S. Son, J. B. Park, and Y. H. Joo, “Segmentalized FCM-based tracking algorithm for zigzag maneuvering target,” International Journal of Control, Automation and Systems, vol. 13, no. 1, pp. 231–237, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. N. Cai and L. Xue, “Clustering by group consensus of unstable dynamic linear high-order multi-agent systems,” in Proceedings of the 34th Chinese Control Conference (CCC '15), pp. 7212–7216, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Guo, H. Pan, and X. Nian, “Adaptive pinning control of cluster synchronization in complex networks with Lurie-type nonlinear dynamics,” Neurocomputing, vol. 182, pp. 294–303, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Xiao and L. Wang, “Consensus problems for high dimensional multi-agent systems,” IET Control Theory & Applications, vol. 1, no. 3, pp. 830–837, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Wang, D. Cheng, and X. Hu, “Consensus of multi-agent linear dynamic systems,” Asian Journal of Control, vol. 10, no. 2, pp. 144–155, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. J. Xi, M. He, H. Liu, and J. Zheng, “Admissible output consensualization control for singular multi-agent systems with time delays,” Journal of the Franklin Institute. Engineering and Applied Mathematics, vol. 353, no. 16, pp. 4074–4090, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. Z. Li, Z. Duan, G. Chen, and L. Huang, “Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 57, no. 1, pp. 213–224, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  28. N. Cai, J.-X. Xi, and Y.-S. Zhong, “Swarm stability of high-order linear time-invariant swarm systems,” IET Control Theory & Applications, vol. 5, no. 2, pp. 402–408, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  29. W. Ren and R. W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 655–661, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  30. R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge University Press, Cambridge, UK, 1985. View at Publisher · View at Google Scholar · View at MathSciNet