Design of experiments in neuro-fuzzy systems

Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown. These systems are robust solutions that search for representation of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and the definition of parameters effectiveness of these systems is a hard task yet. In this paper we perform a statistical analysis to verify the interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis carries out using a powerful statistical tool, the design of experiments (DOE) in two neuro-fuzzy models, adaptive neuro fuzzy inference system (ANFIS) and evolving fuzzy neural networks (EFuNN).