System identification: theory for the user
System identification: theory for the user
Qualitative process theory using linguistic variables
Qualitative process theory using linguistic variables
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computers and Biomedical Research
Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
How to improve fuzzy-neural system modeling by means of qualitative simulation
IEEE Transactions on Neural Networks
The Knowledge Engineering Review
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The most problematic and challenging issues in fuzzy modeling of nonlinear system dynamics deal with robustness and interpretability. Traditional data-driven approaches, especially when the data set is not adequate, may lead to a model that results to be either unable to reproduce the system dynamics or numerically unstable or unintelligible. This paper demonstrates that Qualitative Reasoning plays a crucial role to significantly improve both robustness and interpretability. In the modeling framework we propose both fuzzy partition of input-output variables and the fuzzy rule base are built on the available deep knowledge represented through qualitative models. This leads to a clear and neat model structure that does describe the system dynamics, and the parameters of which have a physically significant meaning. Moreover, it allows us to properly constrain the parameter optimization problem, with a consequent gain in numerical stability. The obtained substantial improvement of model robustness and interpretability in “actual” physical terms lays the groundwork for new application perspectives of fuzzy models.