Some properties of the assembly neural networks

Generalization phenomena which take place in two different assembly neural networks are considered in the paper. Either of these two assembly networks is artificially partitioned into several subnetworks according to the number of classes that the network has to recognize. Hebb's assemblies are formed in the networks. One of the assembly networks is with binary connections, the other is with analog ones. Recognition abilities of the networks are compared on the task of handwritten character recognition. The third neural network of a perceptron type is considered in the paper for comparison with the previous ones. This latter network works according to the nearest-neighbor method. Computer simulation of all three neural networks was performed. Experiments showed that the assembly network with binary connections has approximately the same recognition accuracy as the network realizing the nearest-neighbor technique.