Abstract
A benchmark study of self-organizing neural network models is conducted. The comparison of advantages and disadvantages of unsupervised learning artificial neural networks are discussed. The unsupervised learning artificial neural networks discussed in this paper include adaptive resonance theory (ART2), DIGNET, self-organizing feature map, and learning vector quantization (LVQ). For the benchmark study of artificial neural network applications on data clustering and pattern recognition problems with additive gaussian noise, we compare the performance of the unsupervised learning systems, ART2 and DIGNET. Results of computer simulation show that both ART2 and DIGNET achieve good performance on pattern clustering, but DIGNET is faster in the learning process and has better results on the overall clustering performance.
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© 1993 Springer-Verlag Berlin Heidelberg
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Wann, CD., Thomopoulos, S.C.A. (1993). Comparative study of self-organizing neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_166
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DOI: https://doi.org/10.1007/3-540-56798-4_166
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