Quantile based decision making rule of the neural networks committee for ill-posed approximation problems

In this paper a multidimensional function approximation problem is stated. This problem is characterized by the strong influence of arguments measurement errors on the accuracy of the function estimation and a small amount of train data. A neural networks based solution is used for this problem.To improve the accuracy of the approximation model it is proposed to use a neural networks committee with an original decision making rule for the construction of the generalized function estimate. The developed rule is based on a specially introduced indirect accuracy measure and α-quantileα-quantile calculation of its probability level. The decision making rule is trained on a set of patterns and uses statistical properties of each pattern's processing by the committee's networks.The computational scheme of the proposed approximation model and the effectiveness of the proposed approach are demonstrated on a simple model example. The developed method was successfully applied to a real industrial problem of metal's hardness characteristics estimation on the basis of kinetic indentation data. The results of modeling experiments are discussed.