Analysis of the learning model for KYDON system

In this paper, a learning model for an autonomous vision multi-layer architecture, called KYDON, is presented modeled and analyzed. This learning model uses a birth and death approach to derive the relationships among the parameters used in the learning characteristic function. In addition the two critical (deletion and saturation) points on the learning curve are evaluated. These points represent two extreme states on the learning process. The KYDON architecture consists of `k´ layers of array processors. The lowest layers consist of lower-level processing layers, and the rest consist of higher-level processing layers. The interconnectivity of the PEs in each array is based on a full hexagonal mesh structure. KYDON uses graph models to represent and process the knowledge, extracted from the image. The knowledge base of KYDON is distributed among its PE´s