Abstract
We suggest an effective algorithm based on q −state Potts model providing an exponential growth of network storage capacity M ~N 2S + 1, where N is the dimension of the binary patterns and S is the free parameter of task. The algorithm allows us to identify a large number of highly distorted similar patterns. The negative influence of correlations of the patterns is suppressed by choosing a sufficiently large value of the parameter S. We show the efficiency of the algorithm by the example of a perceptron identifier, but it also can be used to increase the storage capacity of full connected systems of associative memory. Restrictions on S are discussed.
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Kryzhanovsky, V., Kryzhanovsky, B., Fonarev, A. (2008). Application of Potts-Model Perceptron for Binary Patterns Identification. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_57
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DOI: https://doi.org/10.1007/978-3-540-87536-9_57
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