An Adaptive Self-Organizing Algorithm with Virtual Connection

Abstract

In this paper, we propose an unsupervised learning algorithm that uses the concept of virtual connection. Performing some numerical experiments, we have confirmed that the algorithm has important functions: classification of the input space, labeling of the classified input space, and flexible adaptation to time-variant input space. The algorithm is suited for hardware implementation. An application to vector quantization is also discussed.