Igor P. Muraviev

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A sparsely encoded Hopjield-like attractor neural network is investigated analytically and by computer simulation. Informational capacity and recall quality are evaluated. Three analytical approaches are used: replica method (RM); method of statistical neurodynamics (SN); and single-step approximation (SS). Computer simulation conjirmed the good accuracy of(More)
A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a(More)
The unsupervised learning of feature extraction in high-dimesional patterns is a central problem for the neural network approach. Feature extraction is a procedure which maps original patterns into the feature (or factor) space of reduced dimension. In this paper we demonstrate that Hebbian learning in Hopfield-like neural network is a natural procedure for(More)
The paper develops a model in which two manufacturers bid for representation by each of two available retailers who then choose noncooperatively which manufacturer's bid, if any, to accept. This framework allows for interlocking relationships : each manufacturer can employ both retailers and conversely each retailer can represent both manufacturers. In(More)
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