Method of training a neural network, including receiving sets of digital attributes representing multidimensional regression at inputs; expanding the network by adding neurons and defining their activation functions and interconnections; any neuron of the neural network is capable of being directly connected to any other neuron; (i) when a training speed falls below predefined threshold, and accuracy does not improve, identifying a neuron with highest error value; (ii) adding a neuron directly between the identified neuron and a corresponding output; (iii) setting only a connection coefficient between the added neuron and the identified neuron to zero before it is modified by the training, while other coefficients of the added neuron are set the same as coefficients of the identified neuron, before they are modified. After at least one iteration, either (iv) finishing the training of the neural network or (v) continuing to train the network to reach a predefined depth.