
PresentationsOn the training of neural networks using neurons McCulloch  Pitts, by selective Monte CarloREU named after G.V. Plekhanov Education multilayer neural network using neurons McCulloch  Pitts, reduces to the calculation of weight coefficients using the recursive transformation, where  the input signal for subsequent th layer () th layer;  the matrix of weighting factors;  function characterizing the nonlinear transformation threshold;  the threshold value for the ith layer. The matrices are computed for each th layer network using iterative procedures:, where  the increment weights for the iteration number t;  computation error. When. Numerous iterative procedures of different authors: Rosenbluth procedures Widrow, Hebb, backpropagation method errors and others. Instead, an iterative method for determining weighting coefficients in this study suggested their presence by selective Monte Carlo method. According to the proposed method produced a selective determination of random numbers  weighting coefficients in accordance with the codewords of the input signals. The zero value codeword is assigned a random number in the range around zero, unity in the codeword is assigned a weighting value in the range of random numbers near the center of 1. 7. Matlab software was used to generate random numbers to obtain random numbers from an arbitrary range [a, b] has been used as operator + (ba) * rand. Formation of selective characteristics of random numbers produced by the sensor code combinations of input signals, as described above. As an example, detection of numbers 0 10 were considered, 1, ..., 9 for 4x6 screen. Each of the 10 digits corresponding to a sequence of 24 zeros and ones, discrimination is implemented with a singlelayer neural network neurons ten registering McCulloch  Pitts. The total number of weighting factors equal to 240. In calculating the number of realizations of random numbers with uniform distribution varied within (1200000). When using an iterative procedure for calculating weighting coefficients duration calculation order of magnitude greater.
