A robust model structure selection method for small sample size and multiple datasets problems

In model identification, the existence of uncertainty normally generates negative impact on the accuracy and performance of the identified models, especially when the size of data used is rather small. With a small data set, least squares estimates are biased, the resulting models may not be reliable for further analysis and future use. This study introduces a novel robust model structure selection method for model identification. The proposed method can successfully reduce the model structure uncertainty and therefore improve the model performances. Case studies on simulation data and real data are presented to illustrate how the proposed metric works for robust model identification.