A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

• We propose a new algorithm to nonlinear modelling.
• Our method uses flexible neuro-fuzzy systems and population based algorithms.
• Our method includes various criteria of the interpretability of the fuzzy rules.
• Our method uses Ward′s clustering method to initial population generation.

In this paper we propose a new approach to nonlinear modelling. It uses capabilities ofthe so-called flexible neuro-fuzzy systems and evolutionary algorithms. The aim of our method is not only to achieve appropriate accuracy of the model, but also to ensure the possibility of interpretability of the knowledge within it. The proposed approach was achieved by, among others, appropriate selection of operational criteria applied to evolutionary model creation. It allows to extract interpretable fuzzy rules in the cases which use the learning data e.g. from identification. The possibility of interpretation of knowledge accumulated in the model seems to be important in practice, because it guarantees operation predictability and facilitates production of efficient and accurate control methods. Our method was tested with the use of well-known simulation problems from the literature.