Application of potts-model perceptron for binary patterns identification

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. © Springer-Verlag Berlin Heidelberg 2008.