Learning rate updating schemes of unsupervised learning

This paper first presents the improved convergence method using adjust learning rate (α) and clustering analysis in Kohonen´s network. New learning rate method which adopts hyperexponential function to reduce the influence of order presented of input patterns improves the convergence time in training. We also propose the new error measuring method using Hamming distance between patterns of each class and cluster center, Secondly, new strategy to speed up training the GLVQ is also proposed. Instead of using learning update scheme by Pal et al. (1993), we introduce adaptive learning rate updating strategy. Though Pal et al.´s approach needs a priori information, approach proposed works without external parameters, and convergence time is reduced compared with Pal et al.´s