A Hybrid Input-Output Approach to Model Metabolic Systems: An Application to Intracellular Thiamine Kinetics

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Qualitative-fuzzy system identification of complex dynamical systems

Fuzzy systems have been proved to be excellent candidates for system dynamics identification. However, they are affected by two drawbacks: the resulting nonlinear model (i) does not guarantee that the generalization property holds unless a large amount of samples is employed, and (ii) is not understandable from a physical viewpoint. These drawbacks are particularly serious when fuzzy identification deals with complex natural systems as the observational data set and/or empirical knowledge can occur to be inadequate. For these systems, the available knowledge of the underlying mechanisms is qualitative and highly incomplete, and does often prevent from formulating a quantitative differential model but not a qualitative one. This paper demonstrates that Qualitative Reasoning methods properly integrated with fuzzy systems yield a hybrid system identification method that overcomes the problems outlined above

How to improve fuzzy-neural system modeling by means of qualitative simulation

The main problem in efficiently building robust fuzzy-neural models of nonlinear systems lies in the difficulty to define a "meaningful" fuzzy rule-base. Our approach to the solution of such a problem is based on a hybrid method which integrates fuzzy systems with qualitative models. We introduce qualitative models to exploit the available, although incomplete, a priori physical knowledge on the system with the goal to infer, through qualitative simulation, all of its possible behaviors.We show here that a rule-base, which captures all of the distinctions in the system states, is automatically generated by encoding the knowledge of the system dynamics described by the outcomes of its qualitative simulation. Such a rule-base properly initializes a fuzzy identifier, which is then tuned to a set of experimental data. Our method has shown good performance when applied both as a predictor and as a simulator