Global sensitivity analysis approach for input selection and system identification purposes--a new framework for feedforward neural networks.

A new algorithm for the selection of input variables of neural network is proposed. This new method, applied after the training stage, ranks the inputs according to their importance in the variance of the model output. The use of a global sensitivity analysis technique, extended Fourier amplitude sensitivity test, gives the total sensitivity index for each variable, which allows for the ranking and the removal of the less relevant inputs. Applied to some benchmarking problems in the field of features selection, the proposed approach shows good agreement in keeping the relevant variables. This new method is a useful tool for removing superfluous inputs and for system identification.