Structure of an ART1 network in SNNS. Thin arrows represent a connection from one unit to another. Fat arrows which go from a layer to a unit indicate that each unit of the layer is connected to the target unit. Similarly a fat arrow from a unit to a layer means that the source unit is connected to each of the units in the target layer. The two big arrows in the middle represent the full connection between comparison and recognition layer and the one between delay and comparison layer, respectively.
The topology of ART1 networks in SNNS has been chosen to to perform most of the ART1 algorithm within the network itself. This means that the mathematics is realized in the activation and output functions of the units. The idea was to keep the propagation and training algorithm as simple as possible and to avoid procedural control components.
In figure the units and links of ART1 networks in SNNS are displayed.
The F or input layer (labeled inp in figure ) is a set of N input units. Each of them has a corresponding unit in the F or comparison layer (labeled cmp). The M elements in the F layer are split into three levels. So each F element consists of three units. One recognition ( rec) unit, one delay ( del) unit and one local reset ( rst) unit. These three parts are necessary for different reasons. The recognition units are known from the theory. The delay units are needed to synchronize the network correctly. Besides, the activated unit in the delay layer shows the winner of F . The job of the local reset units is to block the actual winner of the recognition layer in case of a reset.
Finally, there are several special units. The cl unit gets positive activation when the input pattern has been successfully classified. The nc unit indicates an unclassifiable pattern, when active. The gain units g and g with their known functions and at last the units ri (reset input), rc (reset comparison), rg (reset general) and (vigilance), which realize the reset function.
For an exact definition of the required topology for ART1 networks in SNNS see section