Neural network models based on small data sets

Abstract

In this paper, we attempt, using an artificial intelligence method based on neural networks, to obtain a model of a nonlinear process from observed datasets. In the first part of the paper, six different processes are analyzed on the basis of small data sets and divided into two groups. After that, the corresponding data-based models are generated for the obtained two groups of measured data sets. In the following, the proposed models are tested on two new data sets.