Abstract
The K-means Fast Learning Artificial Neural Network (KFLANN) is a small neural network bearing two types of parameters, the tolerance, δ and the vigilance, μ. In previous papers, it was shown that the KFLANN was capable of fast and accurate assimilation of data [12]. However, it was still an unsolved issue to determine the suitable values for δ and μ in [12]. This paper continues to follows-up by introducing Genetic Algorithms as a possible solution for searching through the parameter space to effectively and efficiently extract suitable values to δ and μ. It is also able to determine significant factors that help achieve accurate clustering. Experimental results are presented to illustrate the hybrid GA-KFLANN ability using available test data.
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Xiang, Y., Phuan, A.T.L. (2004). Genetic Algorithm Based K-Means Fast Learning Artificial Neural Network. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_71
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DOI: https://doi.org/10.1007/978-3-540-30549-1_71
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