Abstract
A model of fuzzy Kohonen neural network for fuzzy clustering is presented. It uses fuzzy membership degree to describe approximate degree for input patterns and clusters’ centers, which is represented by learning rate. In addition, in order to extract more useful information from input patterns, a supervised learning, called post-learning phase, is added to adaptive learning. Then the model is applied for a specific clustering’s problem, the result shows it can greatly improve performances of recognition and classification.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ye, S., Qin, X., Cai, H. (2006). Research on Fuzzy Kohonen Neural Network for Fuzzy Clustering. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2006. Lecture Notes in Computer Science, vol 4101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11863649_27
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DOI: https://doi.org/10.1007/11863649_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44494-7
Online ISBN: 978-3-540-44496-1
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