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Our research is related to the hybrid neural network training. It consists of local elements, each of which solving its own problem, and recurrent control element producing the general solution. Local elements have identical perceptron inputs and outputs. It is assumed that the training sample is clustered and each element is trained beforehand and independently. The training of local elements and the control element is considered in detail. The methods of regret assessment for the control element are shown. A computational experiment of the hybrid neural network training is carried out through a recognition problem. As a result of the training, the control element selected the local element with the least loss. AMS Subject Classification: 68T05, 68T42, 62M20, 62M45

SEPARATE TRAINING OF HYBRID NEURAL NETWORK