Knowledge representation by dynamic competitive learning techniques

摘要

The competitive learning technique is a well-known algorithm used in neural networks which classifies the input vectors, so that the vectors (samples) belonging to the same class have similar characteristics. Each class is represented by one unit. Dynamic competitive learning is an unsupervised learning technique consisting of two additional parts related to conventional competitive learning: a method of generation of new units within a cluster and a method of generating new clusters. As seen in a description of the multilayered neural networks, the number of clusters, their connections, and the generation of new units is determined dynamically during learning. The model is capable of high-level storage of complex data structures and their classification, including exception handling.