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)
The problem of finding out the global minimum of a multiextremal functional is discussed. One frequently faces with such a functional in various applications. We propose a procedure, which depends on the dimensionality of the problem polynomially. In our approach we use the eigenvalues and eigenvectors of the connection matrix.
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)