On learning control with limited training data

In this paper, we study the interpolation approach in reducing the problem of small training sample sizes severely affecting the learning control performance of artificial neural networks when the dimension of the input variables is high. We use the local polynomial fitting approach to individually rebuild the time-variant functions of system states. Based on these functions, we can effectively produce new unlabelled training samples. We show that by using additional unlabelled samples, the learning control performance can be improved and, therefore, the overfitting phenomenon can be mitigated. Furthermore, experimental results verified these claims.