Development of A Comprehensive Quality Measure for Identification of Nonlinear Hybrid Systems

In this study, a comprehensive quality measure criterion is developed to evaluate the performance of the identified models for nonlinear hybrid systems using support vector regression-based techniques. The proposed quality measure criterion includes all the factors that affect the quality of the identified models, namely identification error, quality of the switching signal, and model complexity. Using the proposed criterion, the resulting models of hybrid systems identification can be efficiently compared and the best model with acceptable complexity, tolerable identification error, and desirable switching signal quality will be selected. This quality measure criterion prevents selecting the complex models relying on the Occam’s Razor theorem. Besides, it provides the possibility of comparing the effects of different kernel functions on the identified models considering the aforementioned factors.