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Particle swarm optimization based clustering in writing

Particle swarm optimization based clustering in writing adopted to accelerate convergence

  • Chaoshun Li .
  • Jianzhong Zhou
  • Pangao Kou
  • Jian Xiao
  • School of Hydropower and knowledge Engineering, Huazhong College of Science, Wuhan 430074, China

Received 15 This summer 2011. Revised 18 October 2011. Recognized 1 December 2011. Available on the web 5 The month of january 2012. Conveyed by M. Sato-Ilic

Abstract

Clustering is a well-liked data analysis and knowledge mining technique. Within this paper, a manuscript chaotic particle swarm fuzzy clustering (CPSFC) formula according to chaotic particle swarm (CPSO) and gradient technique is suggested. Fuzzy clustering model optimization is challenging, to be able to solve this issue, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC) are introduced, along with a new CPSO formula combined AIWF and ICMIC based chaotic local internet search is studied. The CPSFC formula utilizes CPSO to look the fuzzy clustering model, exploiting the searching capacity of fuzzy c -means (FCM) and staying away from its major limitation of having stuck at in your area optimal values. Meanwhile, gradient operator is adopted to accelerate convergence from the suggested formula. Its brilliance within the FCM formula and the other two global optimization formula-based clustering methods is extensively shown for many artificial and real existence data takes hold comparative experiments.

Keywords

  • Fuzzy clustering model
  • Fuzzy c -means
  • Chaotic particle swarm optimization
  • Gradient method
  • Iterative chaotic map with infinite collapses

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Vitae

Chaoshun Li received the B.S. degree in Thermal Energy and Power Engineering in the Wuhan College, Wuhan, P.R.

China, in 2005, and Ph.D. levels in Water Conservancy and Hydropower Engineering in the Huazhong College of Science (HUST), Wuhan, P.R. China, this year.

He’s presently a Lecturer within the School of Hydropower and knowledge Engineering at HUST. His research interests include fuzzy application system, fuzzy modeling and system identification.

Jianzhong Zhou received the B.S. degree in Automatic Control from Nanjing College of Aeronautics and Astronauticsy, Nanjing, P.R. China, in 1982.

He’s presently a Professor within the School of Hydropower and knowledge Engineering at HUST. His research interests include charge of hydraulic turbine unit, operation of cascade reservoirs.

Pangao Kou received the B.S. degree in Thermal Energy and Power Engineering in the North China College water Sources and Electrical Power, Zhengzhou, P.R. China, in 2007. He’s presently a Ph.D. student in Water Conservancy and Hydropower Engineering from HUST, Wuhan, P.R. China.

His research interests include system identification and charge of hydraulic turbine units.

Jian Xiao received the B.S. degree in Water Conservancy and Hydropower Engineering in the TianJin College, TianJin, P.R. China, in 2007, M.S degree in Fluid Machinery and Engineering in the HoHai College, NanJing, P.R. China, this year. He’s presently a Ph.D. student at HUST, Wuhan, P.R. China.

His research interests include system identification and fault proper diagnosis of hydraulic turbine units.

Particle swarm optimization based clustering in writing Huazhong University

Corresponding author. Tel. +86 2787543992.

2011 Elsevier B.V. All legal rights reserved.

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