An adaptive recognition using self-organized network
Yoshikazu Miyanaga, Koji Tochinai · 2003
An adaptive recognition system that is based on self-organization is proposed. The method estimates the cluster distribution of given data and recognizes an unknown input datum at the same time. The clustering/recognizing of a given characteristic vector is based on the Mahalanobis distance. By using adaptation, it is possible to reconstruct the cluster set suitable for the given characteristic data even if the distribution of these data changes with time. It is also shown that the total number of nodes can be minimized by using the rules of node merging. The adaptability and the generalizability of the clustering and recognition are explored.>