A Process of Generalization in the Assembly Neural Network
An assembly neural network model with a new recognition algorithm is described. The network is artificially partitioned into subnetworks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the subnetworks. Hebb s assemblies are formed in the column structures of the subnetworks by means of modification of connections weights. A generalization process takes place within each subnetwork of the assembly network separately which results in formation of an adequate description of every recognized class inside its own subnetwork. A computer simulation of the network is performed. The generalization phenomenon is explored in special experiments on the character recognition task.
Alexander Goltsev
German National Research Center for Information Technology (GMD)Schloss Birlinghoven, D-53754 Sankt Augustin
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
85-90
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）