Unsupervised general fuzzy min-max artificial neural network
Zhang Qing-gui · Systems engineering and electronics · 2004
An unsupervised general fuzzy min-max(GFMM) artificial neural network is proposed. This network inherits the merit of the general fuzzy min-max network which uses the fuzzy input vectors. Because of the newly added unsupervised learning function, it counteracts the weakness which makes the general fuzzy min-max network be incapable of learning any new pattern class. The results and analyses of the experimental testing indicate that this network will find a wide application in the automatic target recognition in the future.