Abstract
An assembly neural network model is described. The network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. In the process of primary learning Hebb’s neural assemblies are formed in the sub-networks by means of modification of connections’ weights. Then, a differentiation process is executed which significantly improves the recognition accuracy of the network. A computer simulation of the assembly network is performed with the aid of which the differentiation process is studied in a set of experiments on a character recognition task using two types of separate handwritten characters: Ukrainian letters and Arabic numerals of MNIST database.
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© 2004 Springer-Verlag Berlin Heidelberg
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Goltsev, A., Kussul, E., Baidyk, T. (2004). A Process of Differentiation in the Assembly Neural Network. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_69
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DOI: https://doi.org/10.1007/978-3-540-30499-9_69
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