Generalized net model for parallel optimization of multilayer perceptron with momentum backpropagation algorithm

In this paper we used a generalized net which gives a possibility for parallel optimization of multilayer neural networks. For training the backpropagation algorithm with momentum was considered. We proposed a generalized net model of parallel training of two neural networks with different architectures. The difference between the networks is in the number of neurons in main difference of the neural networks architectures is the numbers of neurons in hidden layers. In result we can obtain optimal neural network architecture.