Efficient Selection of Inputs for Artificial Neural Network Models

The selection of an appropriate subset of variables from a set of measured potential input variables for inclusion as inputs to model the system under investigation is a vital step in model development. This is particularly important in data driven techniques, such as artificial neural networks (ANNs) and fuzzy systems, as the performance of the final model is heavily dependent on the input variables used to develop the model. Selection of the best set of input variables is essential to being able to model the system under consideration reliably. When the available data set is high dimensional, it is necessary to select a subset of the potential input variables to reduce the number of free parameters in the model in order to obtain good generalization with finite data. The correct choice of model inputs is also important for improving computational efficiency. However, the topic of input selection is a difficult one. Real systems are generally complex and mostly associated with nonlinear processes. Consequently, the dependencies between output and input variables, as well as conditional dependencies between variables, are difficult to measure.