Data-driven Modelling of Engineering Systems with Small Data, a Comparative Study

This paper equitably compares five different Artificial Intelligence (AI) models and a linear model to tackle two real-world engineering data-driven modelling problems with small number of experimental data samples, one with sparse and one with dense data. The models of both cases are shown to be highly nonlinear. In the case with available dense data, Multi-Layer Perceptron (MLP) evidently outperforms other AI models and challenges the claims in the literature about superiority of Fully Connected Cascade (FCC). However, the results of the problem with sparse data shows superiority of FCC, closely followed by MLP and neuro-fuzzy network.