Повышение обобщающей способности нейронных сетей и селекция обучающих выборок

Evaluating the number of hidden neurons necessary for solving of pattern recognition and classification tasks is one of the key problems in artificial neural networks. Multilayer perceptron is the most useful artificial neural network to estimate the functional structure in classification. In this note, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve classification and pattern problems with arbitrary accuracy. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. A direct relationship between the compactness estimates and the generalizing ability of neural networks is experimentally proved. It is experimentally proved that the minimum number of hidden layers, the number of neurons in hidden layers, and the removal of noise objects affect the indicators of the generalizing ability of neural networks.

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