A model validation scale based on multiple indices

Abstract

Validation of an estimated model is not a trivial task because it depends on the purpose of the model, which usually defines the most important features of the model. Thus, in a validation process, the use of diverse tools that exploit different domains is recommended. Here, with this aim, a scale for model validation is proposed that combines the Normalized Root Mean Square Error (NRMSE) with two new indices: the coherence-based index and the fourth-order cross-cumulant index. The proposed scale was used for the validation of three models: the Logistic Map, the Duffing–Ueda oscillator, and the Buck converter. The results demonstrated that the proposed model validation scale produces a more complete validation process that takes into account both time and frequency information and provides robustness against Gaussian noise.