Abstract
Due to the localization of the objective function of traditional fuzzy clustering algorithm, a novel adaptive objective function of fuzzy clustering is proposed, the new objective function integrates the clustering characteristic of input space and the real time approximate characteristic of output space. The extraordinary neural network to handle the fuzzy clustering algorithm is also proposed. The experimental results show that the new algorithm has better performance in stable convergent rate, convergent speed, and the initial condition sensitivity compared with traditional fuzzy clustering algorithm. The result illuminates the rationality of importing felicitous adaptive feedback factors into the objective function.
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References
H. Pilian, H. Yuexian. An Asynmmetric Robust Learnning Algorithm of Fuzzy Clustering Neural Network. Journal of Computer Research and Development. (2001) 38,296–301.
A. Staiano, R. Tagliaferri, W. Pedrycz. Improving RBF Networks Performance in Regression Tasks by Means of a Supervised Fuzzy Clustering. Neurocomputing. (2006) 69,1570–1581.
B. Kosko. Fuzzy Engineering. Publish of Xi’an Jiaotong University, Prentice-Hall, Englewood Cliffs, NJ (1996) 118–126, 60–72.
D. Zhaohong, W. Shitong. Robust Fuzzy Clustering Neural Network. Journal of Software. (2005) 16,1415–1422.
H. Qinglan, M. Yunxian. Distribution Center: BP Artificial Neural Network Based Site Selection. China Software Science.(2006) 6, 140–143.
D. Zhang, S.K. Pal. A Fuzzy Clustering Neural Network (FCNs) System Design Methodology. IEEE Transaction on Neural Network. (2000) 11,1174–1177.
X. Tang, X. Gao, J. Liu. A Spatial-Temporal Approach for Video Caption Detection and Recognition. IEEE Transaction on Neural Network.(2002) 13,961–997.
W. Pedrycz, G. Vukovich. Logic-oriented Fuzzy Clustering. Pattern Recognition Letters.(2002) 23,1515–1527.
J. Abonyi, F. Szeifert. Supervised Fuzzy for the Identification of Fuzzy Classifiers. Pattern Recoginition Letters.(2003) 24,2195–2220.
A. Bensaid, L.O. Hall, J.C. Bezdek, L.P. Clarke. Partially Supervised Clustering for Image Segmentation. Pattern Recognition. (1996) 29,859–871.
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© 2008 Springer-Verlag Berlin Heidelberg
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Bao, F., Pan, Y., Xu, W. (2008). Adaptive Fuzzy Clustering Neural Network. In: Wang, R., Shen, E., Gu, F. (eds) Advances in Cognitive Neurodynamics ICCN 2007. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8387-7_174
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DOI: https://doi.org/10.1007/978-1-4020-8387-7_174
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8386-0
Online ISBN: 978-1-4020-8387-7
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