| 摘要 |
A probabilistic neural network (PNN) comprises a layer L1 of input nodes, a layer L2 of exemplar nodes, a layer L3 of primary Parzen nodes, a layer L4 of sum nodes, and optionally a layer L5 of output nodes. Each exemplar node determines the degree of match between a respective exemplar vector and an input vector and feeds a respective primary Parzen node. The exemplar and primary Parzen nodes are grouped into design classes, with a sum node for each class which combines the outputs of the primary Parzen nodes for that class and feeds a corresponding output node. The network includes for each primary Parzen node (e.g. L3-2-3P) for the design classes a secondary Parzen node (L3-2-3S), the secondary Parzen nodes all feeding a null class sum node (L4-0). Each secondary Parzen node has a Parzen function with a lower peak amplitude and a broader spread than the corresponding primary Parzen node, and is fed from the exemplar node for that primary Parzen node. The secondary Parzen nodes in effect detect input vectors which are "sufficiently different" from the design classes - that is, null class vectors. The network is applicable to banknote recognition and authentication, the null class corresponding to counterfeit banknotes. <IMAGE> |
