Weights set selection method for feed forward neural networks

In this paper is described a method for weights set selection for a feed forward neural network, based on the fault tolerance analysis of the network. For a certain neural network used in a specific problem, one can obtain many weight sets, as a result of backpropagation training algorithm, due to the fact that this algorithm initializes the weights with random numbers. Each time we repeat the training, we obtain a new set of weights. We propose a method to select one of the available training sets of weights, taking into account the fault tolerance of the network. We considered as a typical fault, the fault of the neurons from the hidden layer. We developed a Java application to illustrate the proposed method.