A clustering algorithm for radial basis function neural network initialization

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    In this paper, we propose an Output-Constricted Clustering (OCC) algorithm for Radial Basis Function Neural Network (RBFNN) initialization. OCC first roughly partitions the output based on the required precision and then refinedly clusters data based on the input complexity within each output partition. The main contribution of the proposed clustering algorithm is that we introduce the concept of separability, which is a criterion to judge the suitability of the number of sub-clusters in each output partition. As a result, OCC is able to determine the proper number of sub-clusters with appropriate locations within each output partition by considering both input and output information. The resulting clusters from OCC are used to initialize RBFNN, with proper number and initial locations of for hidden neurons. As a result, RBFNN starting it's learning from a good point, is able to achieve better approximation performance than existing clustering methods for RBFNN initialization. This better performance is illustrated by a number of examples. © 2011 Elsevier B.V.
    Original languageEnglish
    Pages (from-to)144-155
    Number of pages11
    Issue number1
    Publication statusPublished - 1 Feb 2012


    • Clustering
    • Fuzzy c-mean clustering
    • Neural network
    • Radial basis function
    • Radial basis function neural network


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