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Clustering techniques have an influence on the quality and performance of RBF networks. The aim of the paper is to propose and evaluate a similarity-based approach for cluster initialization with the agent-based population learning algorithm used to select prototypes from the obtained clusters. The performance of the RBF network constructed using the proposed clustering and selection approach is analyzed and evaluated. The proposed approach is also compared with different RBF network initialization procedures.
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