Abstract
Designing an effective structure of the RBF network is the task carried-out at the network initialization phase. Usual approach to deal with the problem is to decide on the number of hidden units and to apply a clustering algorithm to calculate cluster centroids. Clustering techniques have a strong influence on the performance of the RBF networks. The paper focuses on the radial basis function neural network initialization problem and the implementation of the kernel-based fuzzy C-means clustering algorithm, as an alternative method for the RBF networks initialization. Performance of the RBFNs initialized using the kernel-based fuzzy clustering algorithm is compared with several other clustering techniques, including k-means, fuzzy C-means and X-means.
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Czarnowski, I., Jędrzejowicz, P. (2016). Kernel-Based Fuzzy C-Means Clustering Algorithm for RBF Network Initialization. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_28
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DOI: https://doi.org/10.1007/978-3-319-39630-9_28
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