Abstract
For more clear understanding of the measurand, enough information must be obtained in a limited time and space. an improved particle swarm optimization (IPSO) is proposed, and the attempt to use an IPSO algorithm for fuzzy clustering to achieve the partition information sets. In the process of partition, the concept of Shannon entropy is introduced. The information set is divided into subsets according to measurement information entropy and the constituted objective function, and its essence is the use of different sensors to obtain a greater amount of information. The method is applied to logging data set partition.
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References
Still, S.: How Many Clusters? An Information-Theoretic Perspective. Neural Computation 16(12), 2483–2506 (2004)
Kennedy, J., Eberhart, R.: Particle swarm optimzation. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Bezdek, C.J.: Fuzzy Models for Pattern Recognition. The Institute of Electrical and Electronics Engineers, Inc., New York (1992)
Delgado, M., Moral, S.: On the concept of possbility - probability consistency. Fuzzy Sets and Systems (21), 311–313 (1987)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems (1), 3–28 (1978)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)
Runkler, T.A., Katz, C.: Fuzzy clustering by particle swarm optimization. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 601–608 (2006)
Su, R.J., Kong, L., Song, S.L., et al.: A new ridgelet neural network training algorithm based on improved particle swarm optimization. In: 2007 IEEE International Conference on Natural Computation, pp. 411–415 (2007)
Song, S.L., Kong, L., Zhang, P., et al.: Improved particle swarm optimization algorithm with accelerating factor. Journal of Harbin Institute of Technology (New Series) 14(suppl. 2), 146–149 (2007)
Song, S.L., Kong, L., Zhang, P., et al.: Particle swarm optimization algorithm based on space mutation and its application. In: 2009 International Workshop on Intelligent Systems and Applications (2009)
Kong, L., Cheng, J.J., Song, S.L.: Bayer material balance computation based on improved particle swarm optimization algorithm. Journal of Huazhong University of Science and Technology (Natural Science Edition) 36(1), 95–98 (2008)
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Su, R., Kong, L., Cheng, J., Song, S. (2011). An Algorithm of Maximum Entropy Fuzzy Clustering Based on Improved Particle Swarm Optimization. In: Jiang, L. (eds) Proceedings of the 2011, International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia. Advances in Intelligent and Soft Computing, vol 111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25188-7_39
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DOI: https://doi.org/10.1007/978-3-642-25188-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25187-0
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