A Qualitative-Fuzzy Framework for Nonlinear Black-Box System Identification

This paper presents a novel approach to nonlinear black-box system identification which combines Qualitative Reasoning (QR) methods with fuzzy logic systems. Such a method aims at building a good initialization of a fuzzy identifier, so that it will converge to the inputoutput relation which captures the nonlinear dynamics of the system. Fuzzy inference procedures should be initialized with a rule-base predefined by the human expert: when such a base is not available or poorly defined, the inference procedure becomes extremely inefficient. Our method aims at solving the problem of the construction of a meaningful rulebase: fuzzy rules are automatically generated by encoding the knowledge of the system dynamics described by the outcomes of its qualitative simulation. Both efficiency and robustness of the method are demonstrated by its application to the identification of the kinetics of Thiamine (vitamin B1) and its phosphoesters in the cells of the intestine tissue.