Bashir M. Magomedov

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The standard method in optimization problems consists in a random search of the global minimum: a neuron network relaxes in the nearest local minimum from some randomly chosen initial configuration. This procedure is to be repeated many times in order to find as deep energy minimum as possible. However the question about the reasonable number of such random(More)
N -dimensional vectors ) ,..., , ( 2 1 N s s s = s will be called configuration vectors. They define N 2 possible states of the system (the configurations), among which the optimal configuration with regard to the objective function ) (s E has to be found. The number of different states increases exponentially, when N increases. Already if N > 50, it is(More)
We propose a domain model of a neural network, in which individual spin-neurons are joined into larger-scale aggregates, the so-called domains. The updating rule in the domain model is defined by analogy with the usual spin dynamics: if the state of a domain in an inhomogeneous local field is unstable, then it flips, in the opposite case its state undergoes(More)
We propose a domain model of a neural network, in which individual spin-neurons are joined into larger-scale aggregates, the so-called domains. The updating rule in the domain model is defined by analogy with the usual spin dynamics: if the state of a domain in an inhomogeneous local field is unstable, then it flips, in the opposite case its state undergoes(More)
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