Abstract
A combined learning algorithm for a self-organizing map (SOM) is proposed. The algorithm accelerates information processing due to the rational choice of the learning rate parameter, and can work when the number of clusters is unknown, as well as when the clusters are overlapping. This is achieved via the introduction of fuzzy inference that determines the level of membership of the classified pattern to each of the available classes. For neighborhood and membership functions, raised cosine is used. This function provides more flexibility and some new properties for the self-learning and clustering procedures.
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Bodyanskiy, Y., Gorshkov, Y., Kolodyazhniy, V., Stephan, A. (2005). Combined Learning Algorithm for a Self-Organizing Map with Fuzzy Inference. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_59
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DOI: https://doi.org/10.1007/3-540-31182-3_59
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