Parallel and adaptive clustering method suitable for a VLSI system

The authors propose a two-functional network in which adaptive networks are implemented for sophisticated recognition and clustering. In the first subnetwork, self-organized clustering is realized. The clustering is based on Mahalanobis distance. The result of the first subnetwork becomes a vector of similarity values between a given input pattern and all patterns of cluster nodes. The second subnetwork consists of nodes associated with specific labels. All connections between the label nodes of the second functional network and the cluster nodes of the first functional network are determined by supervised learning. Every calculation is executed in parallel and pipelined forms. In addition, the proposed network is experimentally shown to provide good performance. In particular, it is shown that handwritten letters can be accurately recognized by using this network