The paper addresses extraction of linguistic fuzzy rules from data, paying specific attention to such properties of the resulting fuzzy model as interpretability and generalization ability. A modeling technique, combining some previously known heuristic modeling approaches, is developed. Experiments of controller identification based on the truck backer-upper application demonstrate that the proposed technique is able to capture the relevant information even if the data sets used for model extraction are insufficient and/or contain noise.
['Andri Riid', 'Ennu Rustern']
Neuro-fuzzy extraction of interpretable fuzzy rules from data